LaptopPricePrediction.ipynb

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  {
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   },
   "source": [
    "# <font color=blue> Estimating Laptop Price Based on User Input Configuration </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4140,
   "metadata": {
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4141,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('laptop_data.csv')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4142,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>AMD A9-Series 9420 3GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>AMD Radeon R5</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.1kg</td>\n",
       "      <td>21312.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.2GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>256GB Flash Storage</td>\n",
       "      <td>Intel Iris Pro Graphics</td>\n",
       "      <td>Mac OS X</td>\n",
       "      <td>2.04kg</td>\n",
       "      <td>114017.6016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>61735.5360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia GeForce MX150</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.3kg</td>\n",
       "      <td>79653.6000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>Acer</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 8250U 1.6GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel UHD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.6kg</td>\n",
       "      <td>41025.6000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0           0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1           1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2           2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3           3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4           4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "5           5    Acer   Notebook    15.6                            1366x768   \n",
       "6           6   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "7           7   Apple  Ultrabook    13.3                            1440x900   \n",
       "8           8    Asus  Ultrabook    14.0                   Full HD 1920x1080   \n",
       "9           9    Acer  Ultrabook    14.0         IPS Panel Full HD 1920x1080   \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",
       "5     AMD A9-Series 9420 3GHz   4GB            500GB HDD   \n",
       "6        Intel Core i7 2.2GHz  16GB  256GB Flash Storage   \n",
       "7        Intel Core i5 1.8GHz   8GB  256GB Flash Storage   \n",
       "8  Intel Core i7 8550U 1.8GHz  16GB            512GB SSD   \n",
       "9  Intel Core i5 8250U 1.6GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu       OpSys  Weight        Price  \n",
       "0  Intel Iris Plus Graphics 640       macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000       macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620       No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455       macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650       macOS  1.37kg   96095.8080  \n",
       "5                 AMD Radeon R5  Windows 10   2.1kg   21312.0000  \n",
       "6       Intel Iris Pro Graphics    Mac OS X  2.04kg  114017.6016  \n",
       "7        Intel HD Graphics 6000       macOS  1.34kg   61735.5360  \n",
       "8          Nvidia GeForce MX150  Windows 10   1.3kg   79653.6000  \n",
       "9        Intel UHD Graphics 620  Windows 10   1.6kg   41025.6000  "
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   "source": [
    "df.head(10)"
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     "data": {
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       "(1303, 12)"
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    "df.shape"
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       "Index(['Unnamed: 0', 'Company', 'TypeName', 'Inches', 'ScreenResolution',\n",
       "       'Cpu', 'Ram', 'Memory', 'Gpu', 'OpSys', 'Weight', 'Price'],\n",
       "      dtype='object')"
      ]
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     "execution_count": 4144,
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   "source": [
    "df.columns"
   ]
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Unnamed: 0        1303 non-null   int64  \n",
      " 1   Company           1303 non-null   object \n",
      " 2   TypeName          1303 non-null   object \n",
      " 3   Inches            1303 non-null   float64\n",
      " 4   ScreenResolution  1303 non-null   object \n",
      " 5   Cpu               1303 non-null   object \n",
      " 6   Ram               1303 non-null   object \n",
      " 7   Memory            1303 non-null   object \n",
      " 8   Gpu               1303 non-null   object \n",
      " 9   OpSys             1303 non-null   object \n",
      " 10  Weight            1303 non-null   object \n",
      " 11  Price             1303 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(9)\n",
      "memory usage: 122.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Inches</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1303.00000</td>\n",
       "      <td>1303.000000</td>\n",
       "      <td>1303.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>651.00000</td>\n",
       "      <td>15.017191</td>\n",
       "      <td>59870.042910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>376.28801</td>\n",
       "      <td>1.426304</td>\n",
       "      <td>37243.201786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>10.100000</td>\n",
       "      <td>9270.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>325.50000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>31914.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>651.00000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>52054.560000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>976.50000</td>\n",
       "      <td>15.600000</td>\n",
       "      <td>79274.246400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1302.00000</td>\n",
       "      <td>18.400000</td>\n",
       "      <td>324954.720000</td>\n",
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      "text/plain": [
       "       Unnamed: 0       Inches          Price\n",
       "count  1303.00000  1303.000000    1303.000000\n",
       "mean    651.00000    15.017191   59870.042910\n",
       "std     376.28801     1.426304   37243.201786\n",
       "min       0.00000    10.100000    9270.720000\n",
       "25%     325.50000    14.000000   31914.720000\n",
       "50%     651.00000    15.600000   52054.560000\n",
       "75%     976.50000    15.600000   79274.246400\n",
       "max    1302.00000    18.400000  324954.720000"
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>112</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Full HD / Touchscreen 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>Windows 10</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>74538.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>486</td>\n",
       "      <td>Dell</td>\n",
       "      <td>Workstation</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6820HQ 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia Quadro M620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.17kg</td>\n",
       "      <td>124568.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>1083</td>\n",
       "      <td>HP</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel 4K Ultra HD 3840x2160</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.3kg</td>\n",
       "      <td>109170.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>Lenovo</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>4GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.9kg</td>\n",
       "      <td>29250.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>199</td>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>17.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 8550U 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia GeForce 930MX</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.5kg</td>\n",
       "      <td>55677.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>832</td>\n",
       "      <td>Asus</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>4GB</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Nvidia GeForce 920</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.1kg</td>\n",
       "      <td>27652.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>361</td>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</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>Windows 10</td>\n",
       "      <td>2kg</td>\n",
       "      <td>50349.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>484</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 6006U 2.0GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.3kg</td>\n",
       "      <td>30103.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7300HQ 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.5kg</td>\n",
       "      <td>53226.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>1215</td>\n",
       "      <td>Acer</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>11.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core 3205U 1.5GHz</td>\n",
       "      <td>2GB</td>\n",
       "      <td>32GB SSD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Chrome OS</td>\n",
       "      <td>1.3kg</td>\n",
       "      <td>9270.72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 Company            TypeName  Inches  \\\n",
       "112          112  Lenovo  2 in 1 Convertible    13.3   \n",
       "486          486    Dell         Workstation    15.6   \n",
       "1083        1083      HP           Ultrabook    13.3   \n",
       "68            68  Lenovo            Notebook    15.6   \n",
       "199          199      HP            Notebook    17.3   \n",
       "832          832    Asus            Notebook    15.6   \n",
       "361          361    Asus            Notebook    14.0   \n",
       "484          484  Lenovo            Notebook    15.6   \n",
       "21            21  Lenovo              Gaming    15.6   \n",
       "1215        1215    Acer             Netbook    11.6   \n",
       "\n",
       "                               ScreenResolution  \\\n",
       "112   IPS Panel Full HD / Touchscreen 1920x1080   \n",
       "486                 IPS Panel Full HD 1920x1080   \n",
       "1083            IPS Panel 4K Ultra HD 3840x2160   \n",
       "68                            Full HD 1920x1080   \n",
       "199                           Full HD 1920x1080   \n",
       "832                           Full HD 1920x1080   \n",
       "361                           Full HD 1920x1080   \n",
       "484                                    1366x768   \n",
       "21                  IPS Panel Full HD 1920x1080   \n",
       "1215                                   1366x768   \n",
       "\n",
       "                                       Cpu   Ram                Memory  \\\n",
       "112             Intel Core i5 7200U 2.5GHz   8GB             256GB SSD   \n",
       "486            Intel Core i7 6820HQ 2.7GHz  16GB             512GB SSD   \n",
       "1083            Intel Core i7 7500U 2.7GHz  16GB             512GB SSD   \n",
       "68              Intel Core i5 7200U 2.5GHz   4GB               1TB HDD   \n",
       "199             Intel Core i7 8550U 1.8GHz   8GB             256GB SSD   \n",
       "832             Intel Core i5 7200U 2.5GHz   4GB               1TB HDD   \n",
       "361             Intel Core i5 7200U 2.5GHz   8GB             256GB SSD   \n",
       "484             Intel Core i3 6006U 2.0GHz   4GB             128GB SSD   \n",
       "21             Intel Core i5 7300HQ 2.5GHz   8GB  128GB SSD +  1TB HDD   \n",
       "1215  Intel Celeron Dual Core 3205U 1.5GHz   2GB              32GB SSD   \n",
       "\n",
       "                          Gpu       OpSys  Weight      Price  \n",
       "112     Intel HD Graphics 620  Windows 10  1.37kg   74538.72  \n",
       "486        Nvidia Quadro M620  Windows 10  2.17kg  124568.64  \n",
       "1083    Intel HD Graphics 620  Windows 10   1.3kg  109170.72  \n",
       "68      Intel HD Graphics 620  Windows 10   1.9kg   29250.72  \n",
       "199     Nvidia GeForce 930MX   Windows 10   2.5kg   55677.60  \n",
       "832        Nvidia GeForce 920       Linux   2.1kg   27652.32  \n",
       "361     Intel HD Graphics 620  Windows 10     2kg   50349.60  \n",
       "484     Intel HD Graphics 520  Windows 10   2.3kg   30103.20  \n",
       "21    Nvidia GeForce GTX 1050  Windows 10   2.5kg   53226.72  \n",
       "1215        Intel HD Graphics   Chrome OS   1.3kg    9270.72  "
      ]
     },
     "execution_count": 4147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4148,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4149,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          0\n",
       "Company             0\n",
       "TypeName            0\n",
       "Inches              0\n",
       "ScreenResolution    0\n",
       "Cpu                 0\n",
       "Ram                 0\n",
       "Memory              0\n",
       "Gpu                 0\n",
       "OpSys               0\n",
       "Weight              0\n",
       "Price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4150,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Apple', 'HP', 'Acer', 'Asus', 'Dell', 'Lenovo', 'Chuwi', 'MSI',\n",
       "       'Microsoft', 'Toshiba', 'Huawei', 'Xiaomi', 'Vero', 'Razer',\n",
       "       'Mediacom', 'Samsung', 'Google', 'Fujitsu', 'LG'], dtype=object)"
      ]
     },
     "execution_count": 4150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Company'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4151,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>196</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>4K Ultra HD / Touchscreen 3840x2160</td>\n",
       "      <td>Intel Core i7 7820HK 2.9GHz</td>\n",
       "      <td>32GB</td>\n",
       "      <td>1TB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1080</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.49kg</td>\n",
       "      <td>324954.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>830</td>\n",
       "      <td>Razer</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>4K Ultra HD / Touchscreen 3840x2160</td>\n",
       "      <td>Intel Core i7 7820HK 2.9GHz</td>\n",
       "      <td>32GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1080</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.49kg</td>\n",
       "      <td>292986.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>610</td>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel 4K Ultra HD 3840x2160</td>\n",
       "      <td>Intel Xeon E3-1535M v6 3.1GHz</td>\n",
       "      <td>32GB</td>\n",
       "      <td>1TB SSD</td>\n",
       "      <td>Nvidia Quadro M2200M</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.5kg</td>\n",
       "      <td>261018.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>749</td>\n",
       "      <td>HP</td>\n",
       "      <td>Workstation</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Xeon E3-1535M v5 2.9GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia Quadro M2000M</td>\n",
       "      <td>Windows 7</td>\n",
       "      <td>3kg</td>\n",
       "      <td>233845.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>1066</td>\n",
       "      <td>Asus</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>17.3</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 6820HK 2.7GHz</td>\n",
       "      <td>64GB</td>\n",
       "      <td>1TB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 980</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.58kg</td>\n",
       "      <td>211788.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>290</td>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core 3205U 1.5GHz</td>\n",
       "      <td>2GB</td>\n",
       "      <td>16GB SSD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Chrome OS</td>\n",
       "      <td>2.19kg</td>\n",
       "      <td>10602.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>AMD E-Series E2-6110 1.5GHz</td>\n",
       "      <td>2GB</td>\n",
       "      <td>32GB Flash Storage</td>\n",
       "      <td>AMD Radeon R2</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.65kg</td>\n",
       "      <td>10602.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1120</th>\n",
       "      <td>1120</td>\n",
       "      <td>Vero</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Atom X5-Z8350 1.44GHz</td>\n",
       "      <td>4GB</td>\n",
       "      <td>32GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 400</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.35kg</td>\n",
       "      <td>10442.880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Asus</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>11.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Atom x5-Z8350 1.44GHz</td>\n",
       "      <td>2GB</td>\n",
       "      <td>32GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 400</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>0.98kg</td>\n",
       "      <td>10224.432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1215</th>\n",
       "      <td>1215</td>\n",
       "      <td>Acer</td>\n",
       "      <td>Netbook</td>\n",
       "      <td>11.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core 3205U 1.5GHz</td>\n",
       "      <td>2GB</td>\n",
       "      <td>32GB SSD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Chrome OS</td>\n",
       "      <td>1.3kg</td>\n",
       "      <td>9270.720</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0 Company     TypeName  Inches  \\\n",
       "196          196   Razer       Gaming    17.3   \n",
       "830          830   Razer       Gaming    17.3   \n",
       "610          610  Lenovo     Notebook    15.6   \n",
       "749          749      HP  Workstation    17.3   \n",
       "1066        1066    Asus       Gaming    17.3   \n",
       "...          ...     ...          ...     ...   \n",
       "290          290    Acer     Notebook    15.6   \n",
       "31            31    Asus     Notebook    14.0   \n",
       "1120        1120    Vero     Notebook    13.3   \n",
       "20            20    Asus      Netbook    11.6   \n",
       "1215        1215    Acer      Netbook    11.6   \n",
       "\n",
       "                         ScreenResolution  \\\n",
       "196   4K Ultra HD / Touchscreen 3840x2160   \n",
       "830   4K Ultra HD / Touchscreen 3840x2160   \n",
       "610       IPS Panel 4K Ultra HD 3840x2160   \n",
       "749           IPS Panel Full HD 1920x1080   \n",
       "1066          IPS Panel Full HD 1920x1080   \n",
       "...                                   ...   \n",
       "290                              1366x768   \n",
       "31                               1366x768   \n",
       "1120                    Full HD 1920x1080   \n",
       "20                               1366x768   \n",
       "1215                             1366x768   \n",
       "\n",
       "                                       Cpu   Ram              Memory  \\\n",
       "196            Intel Core i7 7820HK 2.9GHz  32GB             1TB SSD   \n",
       "830            Intel Core i7 7820HK 2.9GHz  32GB           512GB SSD   \n",
       "610          Intel Xeon E3-1535M v6 3.1GHz  32GB             1TB SSD   \n",
       "749          Intel Xeon E3-1535M v5 2.9GHz  16GB           256GB SSD   \n",
       "1066           Intel Core i7 6820HK 2.7GHz  64GB             1TB SSD   \n",
       "...                                    ...   ...                 ...   \n",
       "290   Intel Celeron Dual Core 3205U 1.5GHz   2GB            16GB SSD   \n",
       "31             AMD E-Series E2-6110 1.5GHz   2GB  32GB Flash Storage   \n",
       "1120           Intel Atom X5-Z8350 1.44GHz   4GB  32GB Flash Storage   \n",
       "20             Intel Atom x5-Z8350 1.44GHz   2GB  32GB Flash Storage   \n",
       "1215  Intel Celeron Dual Core 3205U 1.5GHz   2GB            32GB SSD   \n",
       "\n",
       "                          Gpu       OpSys  Weight       Price  \n",
       "196   Nvidia GeForce GTX 1080  Windows 10  3.49kg  324954.720  \n",
       "830   Nvidia GeForce GTX 1080  Windows 10  3.49kg  292986.720  \n",
       "610      Nvidia Quadro M2200M  Windows 10   2.5kg  261018.720  \n",
       "749      Nvidia Quadro M2000M   Windows 7     3kg  233845.920  \n",
       "1066  Nvidia GeForce GTX 980   Windows 10  3.58kg  211788.000  \n",
       "...                       ...         ...     ...         ...  \n",
       "290         Intel HD Graphics   Chrome OS  2.19kg   10602.720  \n",
       "31              AMD Radeon R2  Windows 10  1.65kg   10602.720  \n",
       "1120    Intel HD Graphics 400  Windows 10  1.35kg   10442.880  \n",
       "20      Intel HD Graphics 400  Windows 10  0.98kg   10224.432  \n",
       "1215        Intel HD Graphics   Chrome OS   1.3kg    9270.720  \n",
       "\n",
       "[1303 rows x 12 columns]"
      ]
     },
     "execution_count": 4151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=['Price'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4152,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>71378.6832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34kg</td>\n",
       "      <td>47895.5232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86kg</td>\n",
       "      <td>30636.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16GB</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83kg</td>\n",
       "      <td>135195.3360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8GB</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37kg</td>\n",
       "      <td>96095.8080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu   Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz   8GB            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz   8GB  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz   8GB            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz  16GB            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz   8GB            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  \n",
       "0  Intel Iris Plus Graphics 640  macOS  1.37kg   71378.6832  \n",
       "1        Intel HD Graphics 6000  macOS  1.34kg   47895.5232  \n",
       "2         Intel HD Graphics 620  No OS  1.86kg   30636.0000  \n",
       "3            AMD Radeon Pro 455  macOS  1.83kg  135195.3360  \n",
       "4  Intel Iris Plus Graphics 650  macOS  1.37kg   96095.8080  "
      ]
     },
     "execution_count": 4152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['Unnamed: 0'],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4153,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].str.replace('GB','')\n",
    "df['Weight'] = df['Weight'].str.replace('kg','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4154,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>IPS Panel Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>IPS Panel Quad HD+ / Touchscreen 3200x1800</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>2</td>\n",
       "      <td>64GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i7 6500U 2.5GHz</td>\n",
       "      <td>6</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R5 M330</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Celeron Dual Core N3050 1.6GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>500GB HDD</td>\n",
       "      <td>Intel HD Graphics</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1303 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName                            ScreenResolution  \\\n",
       "0      Apple           Ultrabook          IPS Panel Retina Display 2560x1600   \n",
       "1      Apple           Ultrabook                                    1440x900   \n",
       "2         HP            Notebook                           Full HD 1920x1080   \n",
       "3      Apple           Ultrabook          IPS Panel Retina Display 2880x1800   \n",
       "4      Apple           Ultrabook          IPS Panel Retina Display 2560x1600   \n",
       "...      ...                 ...                                         ...   \n",
       "1298  Lenovo  2 in 1 Convertible   IPS Panel Full HD / Touchscreen 1920x1080   \n",
       "1299  Lenovo  2 in 1 Convertible  IPS Panel Quad HD+ / Touchscreen 3200x1800   \n",
       "1300  Lenovo            Notebook                                    1366x768   \n",
       "1301      HP            Notebook                                    1366x768   \n",
       "1302    Asus            Notebook                                    1366x768   \n",
       "\n",
       "                                       Cpu Ram               Memory  \\\n",
       "0                     Intel Core i5 2.3GHz   8            128GB SSD   \n",
       "1                     Intel Core i5 1.8GHz   8  128GB Flash Storage   \n",
       "2               Intel Core i5 7200U 2.5GHz   8            256GB SSD   \n",
       "3                     Intel Core i7 2.7GHz  16            512GB SSD   \n",
       "4                     Intel Core i5 3.1GHz   8            256GB SSD   \n",
       "...                                    ...  ..                  ...   \n",
       "1298            Intel Core i7 6500U 2.5GHz   4            128GB SSD   \n",
       "1299            Intel Core i7 6500U 2.5GHz  16            512GB SSD   \n",
       "1300  Intel Celeron Dual Core N3050 1.6GHz   2   64GB Flash Storage   \n",
       "1301            Intel Core i7 6500U 2.5GHz   6              1TB HDD   \n",
       "1302  Intel Celeron Dual Core N3050 1.6GHz   4            500GB HDD   \n",
       "\n",
       "                               Gpu       OpSys Weight  \n",
       "0     Intel Iris Plus Graphics 640       macOS   1.37  \n",
       "1           Intel HD Graphics 6000       macOS   1.34  \n",
       "2            Intel HD Graphics 620       No OS   1.86  \n",
       "3               AMD Radeon Pro 455       macOS   1.83  \n",
       "4     Intel Iris Plus Graphics 650       macOS   1.37  \n",
       "...                            ...         ...    ...  \n",
       "1298         Intel HD Graphics 520  Windows 10    1.8  \n",
       "1299         Intel HD Graphics 520  Windows 10    1.3  \n",
       "1300             Intel HD Graphics  Windows 10    1.5  \n",
       "1301            AMD Radeon R5 M330  Windows 10   2.19  \n",
       "1302             Intel HD Graphics  Windows 10    2.2  \n",
       "\n",
       "[1303 rows x 9 columns]"
      ]
     },
     "execution_count": 4154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(include='object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4155,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['Ram'] = df['Ram'].astype('int32')\n",
    "df['Weight'] = df['Weight'].astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4156,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1303 entries, 0 to 1302\n",
      "Data columns (total 11 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   Company           1303 non-null   object \n",
      " 1   TypeName          1303 non-null   object \n",
      " 2   Inches            1303 non-null   float64\n",
      " 3   ScreenResolution  1303 non-null   object \n",
      " 4   Cpu               1303 non-null   object \n",
      " 5   Ram               1303 non-null   int32  \n",
      " 6   Memory            1303 non-null   object \n",
      " 7   Gpu               1303 non-null   object \n",
      " 8   OpSys             1303 non-null   object \n",
      " 9   Weight            1303 non-null   float32\n",
      " 10  Price             1303 non-null   float64\n",
      "dtypes: float32(1), float64(2), int32(1), object(7)\n",
      "memory usage: 101.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4157,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company              19\n",
       "TypeName              6\n",
       "Inches               18\n",
       "ScreenResolution     40\n",
       "Cpu                 118\n",
       "Ram                   9\n",
       "Memory               39\n",
       "Gpu                 110\n",
       "OpSys                 9\n",
       "Weight              171\n",
       "Price               791\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Exploratory Data Analysis\n",
    "### initial investigation to discover patterns, spot anomalies, test hypothesis and check assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4158,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4159,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.histplot(df['Price'], kde=True, color='green', fill=True)\n",
    "ax.lines[0].set_color('black')\n",
    "plt.title(\"Categorizing Laptops by Price Range\", fontsize=14)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4160,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 10))\n",
    "ax = sns.countplot(data=df, x='Company')\n",
    "plt.title(\"Number of Laptops Across Different Brands\", fontsize=14)\n",
    "plt.xlabel(\"Company\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.axhline(0, color='black', linewidth=2)\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4161,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sns.barplot(x=df['Company'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title('Laptop Pricing Across Various Brands',fontsize=14)\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4162,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "type_counts = df['TypeName'].value_counts()\n",
    "sorted_order = type_counts.index\n",
    "\n",
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(6, 6))\n",
    "plot = sns.countplot(y='TypeName', data=df, order=sorted_order)\n",
    "\n",
    "for p in plot.patches:\n",
    "    x_position = p.get_width() + 5  # Add a small gap between bar and text\n",
    "    y_position = p.get_y() + p.get_height() / 2  # Center text vertically\n",
    "    count = format(p.get_width(), '.0f')  # Get the count as a string\n",
    "    plot.annotate(count, (x_position, y_position), ha='left', va='center')\n",
    "\n",
    "plt.title(\"Quantifying Laptop Distribution Based on Types\", fontsize=14)\n",
    "plt.xlabel(\"Count\", fontsize=12)\n",
    "plt.ylabel(\"Laptop Type\", fontsize=12)\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4163,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jx47dcjog1UImPj6eyZMn88EHH5gUM2nSJEJDQ0lISACgqKiIPXv28MknnwDQt29fhg4dSlhYGCkpKSxduhS4cXb11KlTKSoqIiUlhdDQUADatWvHtWvXSEtLY8uWLfTq1Qtra2uaNGlCw4YN+e2338q9El95SjeRVatW7ZbzKgkhhCjrVrsYVAuZyMjIMm3/+c9/6Nevn8mms2vXruHo6Ggcbbi4uJCeng5ARkaGcboOW1tbHB0duXr1qkl76XMuX75MRkYGOp2uTLsQQghtVNiO/x07dnDp0iXGjRvHL7/8Ymwv7wjq2+10L2/HUml7ecu61eNvJzU19a6fI4QQoqwKC5l169Zx/PhxevfuTW5uLllZWbz77rvMmTMHvV6PwWDAxsaGzMxM42hEp9ORlZVF/fr1KS4uNk6qqNPpyMzMNO7XKX1OvXr1yMzMNK7z5mXdDQ8PD9lcJoQQd6CgoOC2H8wr7DyZGTNmsH79etauXcv06dPx8PAgOjoaOzs7vLy8jLOjJiQkGGdb9fHxMe67SUpKwsvLCzs7O3x8fFi7di0Ae/fuxd7enoYNG+Lt7U1iYiIGg4GzZ89y5swZnnjiiYrqohBCiL+pFOfJTJ48mfDwcD799FMaNGjA3LlzAQgNDSU8PBx/f39q1aplnChx2LBhTJo0CX9/f6pVq2acEdbX15eDBw8ar6IXGRlJ9erVtemUEEIImVbmZqXDPtlcJoQQd+af3jdlWhkhhLAgu3fvZsyYMezevVvrUoBKsrlMCCGEeSxZsoTjx4+Tm5tLx44dtS5HRjJCCGFJcnNzTb5rTUJGCCGEaiRkhBBCqEZCRgghhGokZIQQQqhGQkYIIYRqJGSEEEKoRkJGCCGEaiRkhBBCqEZCRgghhGokZIQQQqhGQkYIIYRqJGSEEEKoRkJGCCGEaiRkhBBCqEZCRgghhGokZIQQQqhGQkYIIYRqJGSEEEKoRvWQ0ev1BAQEcOHCBQCWL19OQEAAgYGBjBs3jsLCQgCOHDlCv3796NmzJxMmTKC4uBiAtLQ0hgwZgq+vLyNHjiQnJweA7OxsQkJC8PPzY8iQIWRmZgJQWFhIWFgYfn5+BAcHc/LkSbW7KIQQ4hZUDZkDBw4waNAgzpw5A8Dp06f58ssv+e677/j+++8pKSlh2bJlAISFhTFx4kQ2bNiAoijEx8cDMGXKFAYPHkxycjIeHh7ExsYCEB0djZeXF+vXr2fAgAFERkYCEBcXh4ODA+vXr2f8+PGEh4er2UUhhBC3oWrIxMfHM3nyZHQ6HQDVqlXjww8/xNHRESsrK9zd3UlLS+PixYvk5+fj6ekJQN++fUlOTqaoqIg9e/bQs2dPk3aAlJQUAgMDAQgICGDr1q0UFRWRkpJCUFAQAO3atePatWukpaWp2U0hhLgnxUVFWpdw1+62ZluV6gAwji5Kubq64urqCsDVq1dZunQpM2bMICMjAxcXF+PjXFxcSE9P59q1azg6OmJra2vSDpg8x9bWFkdHR65evVrusi5fvkzDhg3V7KoQQtw1Wzs75o573azL/DMrw/jd3MsGGDNj0V09XtWQuZX09HReffVV+vXrR4cOHfjf//5X5jFWVlYoilJu+61YW5c/MLtV+62kpqbe1eOFEOJetG3bVusS7sm+ffvu+LEVHjInT57ktddeY+jQobz88ssA1KtXj6ysLONjMjMz0el0ODs7o9frMRgM2NjYGNsBdDodWVlZ1K9fn+LiYvR6PU5OTuh0OjIzM3FzczNZ1t3w8PDA3t7eTD0WQgjLcnM4FhQU3PaDeYUewqzX63nllVcIDQ01Bgzc2Ixmb29vTMeEhAS8vb2xs7PDy8uLpKQkk3YAHx8fEhISAEhKSsLLyws7Ozt8fHxYu3YtAHv37sXe3l42lQkhhEYqNGRWrlxJVlYWixcvpnfv3vTu3Zv58+cDEBUVxYwZM/Dz8yMvL4/hw4cDMHnyZOLj4+nVqxd79+7l3XffBSA0NJT9+/fj7+/PsmXLmDRpEgDDhg2jsLAQf39/IiMjmT17dkV2UQghxE2slPJ2fDygSod9srlMCFFRzL1zPuXAWXIKiqhpb8czrd3Mumwou+P/n9435Yx/IYQQqpGQEUIIoRoJGSGEEKqRkBFCCKEaCRkhhBCqkZARQgihGgkZIYQQqpGQEUIIoRoJGSGEEKqRkBFCCKEaCRkhhBCqkZARQggLYmNjbfJda5WjCiGEEGbh3sgZ51oOuDdy1roUQKMrYwohhFBHPaea1HOqqXUZRjKSEUIIoRoJGSGEEKqRkBFCCKEaCRkhhBCqkZARQgihGgkZIYQQqpGQEUIIoRoJGSGEEKpRPWT0ej0BAQFcuHABgJ07dxIYGMjzzz/PvHnzjI87cuQI/fr1o2fPnkyYMIHi4mIA0tLSGDJkCL6+vowcOZKcnBwAsrOzCQkJwc/PjyFDhpCZmQlAYWEhYWFh+Pn5ERwczMmTJ9XuohBCiFtQNWQOHDjAoEGDOHPmDAD5+fmMHz+e2NhYkpKSSE1NZcuWLQCEhYUxceJENmzYgKIoxMfHAzBlyhQGDx5McnIyHh4exMbGAhAdHY2Xlxfr169nwIABREZGAhAXF4eDgwPr169n/PjxhIeHq9lFIYQQt6FqyMTHxzN58mR0Oh0ABw8exM3NjcaNG2Nra0tgYCDJyclcvHiR/Px8PD09Aejbty/JyckUFRWxZ88eevbsadIOkJKSQmBgIAABAQFs3bqVoqIiUlJSCAoKAqBdu3Zcu3aNtLQ0NbsphBDiFlSdu6x0dFEqIyMDFxcX422dTkd6enqZdhcXF9LT07l27RqOjo7Y2tqatP99Wba2tjg6OnL16tVyl3X58mUaNmyoWj+FEEKUr0InyFQUpUyblZXVXbffirV1+QOzW7XfSmpq6l09Xggh7kXbtm21LuGe7Nu3744fW6EhU69ePbKysoy3MzIy0Ol0ZdozMzPR6XQ4Ozuj1+sxGAzY2NgY2+HGKCgrK4v69etTXFyMXq/HyckJnU5HZmYmbm5uJsu6Gx4eHtjb25uhx0JUTbt37yY+Pp4XXniBjh07al2OqGRuDseCgoLbfjCv0EOYW7duzenTpzl79iwGg4F169bh7e2Nq6sr9vb2xnRMSEjA29sbOzs7vLy8SEpKMmkH8PHxISEhAYCkpCS8vLyws7PDx8eHtWvXArB3717s7e1lU5kQd2nJkiUcOHCAJUuWaF2KqOIqdCRjb2/PzJkzGTVqFAUFBfj4+ODr6wtAVFQUERER5OTk0KpVK4YPHw7A5MmTCQ8P59NPP6VBgwbMnTsXgNDQUMLDw/H396dWrVpERUUBMGzYMCZNmoS/vz/VqlVj9uzZFdlFISxCbm6uyXch7pWVUt6OjwdU6bBPNpeJB93w4cO5ePEirq6ufPPNN1qXY9Hmjntd6xLuypgZi0xu/9P7ppzxL4QQQjUSMkIIIVQjISOEEEI1EjJCCCFUIyEjhBBCNRIyQgghVCMhI0QVZygs0rqEu1LV6hX3p0JPxhRCmJ9NNTuSho8w6zJzL6cbv5t72b2++cqsyxOVm4xkhBBCqEZCRgghhGokZIQQQqhGQkYIIYRqJGSEEEKoRkJGCCGEaiRkhBBCqEZCRiO7d+9mzJgx7N69W+tShCjDztrK5LsQ90pOxtTIkiVLOH78OLm5uXINdVHpdKntxN7r2XjVqq11KaKKk5DRiFzeVlRmTas70LS6g9ZlCAsgm8uEEEKoRkJGCCGEaiRkhBBCqEaTkFm7di3+/v74+/sza9YsAI4cOUK/fv3o2bMnEyZMoLi4GIC0tDSGDBmCr68vI0eOJCcnB4Ds7GxCQkLw8/NjyJAhZGZmAlBYWEhYWBh+fn4EBwdz8uTJ+663sMhw38uoaFWxZiGE5anwHf95eXlERkaSnJxM7dq1GTRoEDt37uSjjz5i+vTpeHp6Mn78eOLj4xk8eDBTpkxh8ODB+Pv788knnxAbG0tYWBjR0dF4eXnx2WefkZCQQGRkJNHR0cTFxeHg4MD69evZs2cP4eHhrFix4r5qrmZnw+APlprpFbghK+s6AJezrpt92QDLZg8x+zKFEOJuVfhIxmAwUFJSQl5eHsXFxRQXF2Nra0t+fj6enp4A9O3bl+TkZIqKitizZw89e/Y0aQdISUkhMDAQgICAALZu3UpRUREpKSkEBQUB0K5dO65du0ZaWlpFd1MIIQQajGQcHR0JDQ3Fz8+P6tWr0759e+zs7HBxcTE+xsXFhfT0dK5du4ajoyO2trYm7QAZGRnG59ja2uLo6MjVq1dN2kufc/nyZRo2bFiBvRRCCAEahMwff/zBqlWr+Pnnn6lVqxbvv/8+O3bsKPM4KysrFEUpt/1WrK3LH5jdqv1WUlNTTW63bdv2rp5fWezbt0/rEkQFqIp/n/K3eUNV/N3B3f3+Kjxktm/fTqdOnXjooYeAG5vAvvzyS7KysoyPyczMRKfT4ezsjF6vx2AwYGNjY2wH0Ol0ZGVlUb9+fYqLi9Hr9Tg5OaHT6cjMzMTNzc1kWXfDw8MDe3t7M/VYO1X1D1hYPvnbrNpu/v0VFBSU+WB+swrfJ9OiRQt27txJbm4uiqLw008/0b59e+zt7Y3pmJCQgLe3N3Z2dnh5eZGUlGTSDuDj40NCQgIASUlJeHl5YWdnh4+PD2vXrgVg79692NvbV8pNZVY2dibfhRDCEt1RyJSUlPDFF18wduxY9Ho9ixYtwmC4t0Nku3btir+/P3379iUoKIji4mJCQkKIiopixowZ+Pn5kZeXx/DhwwGYPHky8fHx9OrVi7179/Luu+8CEBoayv79+/H392fZsmVMmjQJgGHDhlFYWIi/vz+RkZHMnj37nupUm2PDp7BzrI9jw6e0LkUIIVRzR5vLZs+ezdWrVzl06BAA27ZtIzMzk4iIiHtaaUhICCEhISZtLVq0YOXKlWUe6+rqSlxcXJl2JycnFi5cWKbd3t7eeO5NZWZfpzH2dRprXYYQQqjqjkYyu3btYubMmdjb2+Po6MjixYvL3VkvhBBC3OyOQsbW1tbkCK1q1aoZDysWQgghbuWOksLd3Z2lS5diMBg4deoUS5YsoUWLFmrXJoQQooq7o5HMhAkTOHz4MFeuXGHw4MHk5uYyfvx4tWsTQghVyJVpK84djWQcHR0ZOXIkH330EXq9nnPnzlG3bl21axNCCFXIlWkrzh2NZOLi4njzzTcBuHbtGqNGjbrvSSeFEEIrcmXainNHIbN8+XL++9//AtC4cWMSEhL45ptvVC1MCCFE1XdHIWMwGHB0dDTerlWr1m3nEBNCCCHgDkOmadOmREVFcf78ec6fP8/8+fN59NFHVS5NCCFEVXdHITNlyhTOnDlDnz596N+/P2fOnOHDDz9UuTQhhBBV3R0dXfbwww8TExOjdi1CCCEszG1DJjIykgkTJvDGG2+Ue395c4cJIYQQpW4bMp06dQIwXv5YCCGEuBu3DZnu3bsDN67j8vXXX1dIQUIIUaq4yICtnY3WZdyVqlizmu5on8z169fJzc2lRo0aatcjhBBGtnY2fDSh7CVA7tfVK3rjd3Mvf3xkf7Mur6q7o5BxcHCgW7duNG/e3CRoZJ+MEEKI2/nHkDl27Bg9evSga9eu1K9fvyJqEkIIYSFuGzKrVq1i1qxZuLm5ce7cOaKionj66acrqjYhhBBV3G1DJi4ujsTEROrVq8dvv/3GvHnzJGSEEELcsX88479evXoAtGnThmvXrqlekBBCCMtx25D5+ySYNjZyWJ4QQog7d0dzl5WSmZeFEELcjdvukzl69ChPPfWU8XZ+fj5PPfUUiqJgZWXF//73v3ta6U8//URMTAy5ubl07dqViIgIdu7cyYwZMygoKMDPz4/Ro0cDcOTIESIiItDr9Xh5eTFlyhRsbW1JS0sjLCyMK1eu0KRJE6KioqhZsybZ2dm8//77nD9/HmdnZ6Kjo3FxcbmnOoUQlsnWxs7ku1DPbUcymzZtIjEx0fhVenvdunUkJibe0wrPnz/P5MmTiY2NJTExkd9//50tW7Ywfvx4YmNjSUpKIjU1lS1btgAQFhbGxIkT2bBhA4qiEB8fD9yYGXrw4MEkJyfj4eFBbGwsANHR0Xh5ebF+/XoGDBhAZGTkPdUphLBcTRu1x6l2Q5o2aq91KRbvtiHj6up62697sWnTJnr16kX9+vWxs7Nj3rx5ODg44ObmRuPGjbG1tSUwMJDk5GQuXrxIfn4+np6eAPTt25fk5GSKiorYs2ePcU610naAlJQUAgMDAQgICGDr1q0UFRXdU61CCMv0cF032rbqzcN13bQuxeLd0Rn/5nT27Fns7Ox45ZVXyMzMpFu3bjRr1sxkk5ZOpyM9PZ2MjAyTdhcXF9LT07l27RqOjo7Y2tqatAMmz7G1tcXR0ZGrV68aj5ITQghRcSo8ZAwGA3v37iUuLo4aNWrw5ptv4uDgUOZxVlZWKIpyV+23Ym19V8c3kJqaanK7bdu2d/X8ymLfvn1alyAqQFX8+7zTv82q2DeQ/t2swkPm4YcfplOnTjg7OwPQo0cPkpOTTQ6PzsjIQKfTUa9ePbKysoztmZmZ6HQ6nJ2d0ev1GAwGbGxsjO1wYxSUlZVF/fr1KS4uRq/X4+TkdFc1enh4YG9vf/+d1VhV/QMWls/S/zYfpP4VFBSU+WB+s7v7iG8G3bp1Y/v27WRnZ2MwGNi2bRu+vr6cPn2as2fPYjAYWLduHd7e3ri6umJvb29MzYSEBLy9vbGzs8PLy4ukpCSTdgAfHx8SEhIASEpKwsvLCzs7OYJECCG0UOEjmdatW/Pqq68yePBgioqK6NKlC4MGDaJp06aMGjWKgoICfHx88PX1BSAqKoqIiAhycnJo1aoVw4cPB2Dy5MmEh4fz6aef0qBBA+bOnQtAaGgo4eHh+Pv7U6tWLaKioiq6i0IIIf6/Cg8ZgP79+9O/v+k1Fzp16sT3339f5rEtWrRg5cqy13twdXUlLi6uTLuTk5NcgkAIISqJCt9cJoQQ4sEhISOEEEI1EjJCCCFUIyEjhBBCNRIyQgghVCMhI4QQQjUSMkIIIVQjISOEEEI1EjJCCCFUIyEjhBBCNRIyQgghVCMhI4QQQjUSMkIIIVQjISOEEEI1EjJCCCFUIyEjxD3YvXs3Y8aMYffu3VqXIkSlpslFy4So6pYsWcLx48fJzc2lY8eOWpcjRKUlIxkh7kFubq7JdyFE+SRkhBBCqEZCRqhC9lkIIUD2yQiVyD4LIQTISEaoRPZZCCFA45CZNWsW4eHhABw5coR+/frRs2dPJkyYQHFxMQBpaWkMGTIEX19fRo4cSU5ODgDZ2dmEhITg5+fHkCFDyMzMBKCwsJCwsDD8/PwIDg7m5MmT2nROCCGEdiGza9cu1qxZY7wdFhbGxIkT2bBhA4qiEB8fD8CUKVMYPHgwycnJeHh4EBsbC0B0dDReXl6sX7+eAQMGEBkZCUBcXBwODg6sX7+e8ePHG0NM3FpJcZHWJdy1qlizEA8iTfbJ/Pnnn8ybN4833niDP/74g4sXL5Kfn4+npycAffv2ZcGCBQwYMIA9e/bwySefGNuHDh1KWFgYKSkpLF26FICAgACmTp1KUVERKSkphIaGAtCuXTuuXbtGWloaDRs21KKrVYK1rR37Zr9q1mUWXEs3fjf3sgHafvCF2ZcphDA/TUJm0qRJjB49mkuXLgGQkZGBi4uL8X4XFxfS09O5du0ajo6O2NramrT//Tm2trY4Ojpy9erVcpd1+fLluwqZ1NRUk9tt27a9t45qbN++fXf0ODX6Z29rbfJdDXfaPzUUFBQYv2tZB1TNv08t/zYrgvTv/1R4yKxYsYIGDRrQqVMnVq9eDYCiKGUeZ2Vldcv2W7G2Lv8N7Vbtt+Lh4YG9vf1dPacy0vIP+PnH67Ll9F/4NKmj2jq07F/p34e9vX2VfaPQkqW/Zg9S/woKCsp8ML9ZhYdMUlISmZmZ9O7dm7/++ovc3FysrKzIysoyPiYzMxOdToezszN6vR6DwYCNjY2xHUCn05GVlUX9+vUpLi5Gr9fj5OSETqcjMzMTNzc3k2WJitXSpQYtXWpoXYYQQmMVvuP/q6++Yt26daxdu5Z33nmH7t27M2PGDOzt7Y1DsISEBLy9vbGzs8PLy4ukpCSTdgAfHx8SEhKAG8Hl5eWFnZ0dPj4+rF27FoC9e/dib28v+2OEEEIjleY8maioKGbMmIGfnx95eXkMHz4cgMmTJxMfH0+vXr3Yu3cv7777LgChoaHs378ff39/li1bxqRJkwAYNmwYhYWF+Pv7ExkZyezZs7XqkhBCPPA0PeO/b9++9O3bF4AWLVqwcuXKMo9xdXUlLi6uTLuTkxMLFy4s025vb8+sWbPMX6wQQoi7VmlGMkIIISyPhIwQQgjVSMgIIYRQjYSMEEII1UjICItXWMXmOatq9QpxO3I9GWHxqtna8dJXoWZdZnp2pvG7uZe9ZMR8sy5PCC3JSEYIIYRqJGSEEEKoRkJGCCGEaiRkhBBCqEZCRgghhGokZIQQQqhGQkYIIYRqJGSEEEKoRkJGCCGEaiRkhBBCqEZCRgghhGokZIQQQqhGQkYIIYRqJGSEEEKoRkJGiHtgZWdt8l0IUT5N/kNiYmLw9/fH39+f2bNnA7Bz504CAwN5/vnnmTdvnvGxR44coV+/fvTs2ZMJEyZQXFwMQFpaGkOGDMHX15eRI0eSk5MDQHZ2NiEhIfj5+TFkyBAyMzMrvoPC4tV5sh729WpS58l6WpciRKVW4SGzc+dOtm/fzpo1a0hISODw4cOsW7eO8ePHExsbS1JSEqmpqWzZsgWAsLAwJk6cyIYNG1AUhfj4eACmTJnC4MGDSU5OxsPDg9jYWACio6Px8vJi/fr1DBgwgMjIyIruongAODSqhe65Jjg0qqV1KUJUahUeMi4uLoSHh1OtWjXs7Ox47LHHOHPmDG5ubjRu3BhbW1sCAwNJTk7m4sWL5Ofn4+npCUDfvn1JTk6mqKiIPXv20LNnT5N2gJSUFAIDAwEICAhg69atFBXJ5WyFEEILFR4yzZo1M4bGmTNnSEpKwsrKChcXF+NjdDod6enpZGRkmLS7uLiQnp7OtWvXcHR0xNbW1qQdMHmOra0tjo6OXL16tYJ6J4QQ4ma2Wq34+PHjvP7664wdOxZbW1tOnz5tcr+VlRWKopR53u3ab8Xa+u6yNDU11eR227Zt7+r5lcW+ffvu6HHSv8rnTvsGlt2/qtg3kP7dTJOQ2bdvH++88w7jx4/H39+fX3/9laysLOP9GRkZ6HQ66tWrZ9KemZmJTqfD2dkZvV6PwWDAxsbG2A43RkFZWVnUr1+f4uJi9Ho9Tk5Od1Wfh4cH9vb2ZumrlqrqH/CdsuT+WXLfQPpX1d3cv4KCgjIfzG9W4ZvLLl26xFtvvUVUVBT+/v4AtG7dmtOnT3P27FkMBgPr1q3D29sbV1dX7O3tjamZkJCAt7c3dnZ2eHl5kZSUZNIO4OPjQ0JCAgBJSUl4eXlhZ2dX0d0UQgiBBiOZL7/8koKCAmbOnGlsGzhwIDNnzmTUqFEUFBTg4+ODr68vAFFRUURERJCTk0OrVq0YPnw4AJMnTyY8PJxPP/2UBg0aMHfuXABCQ0MJDw/H39+fWrVqERUVVdFdFEII8f9VeMhEREQQERFR7n3ff/99mbYWLVqwcuXKMu2urq7ExcWVaXdycmLhwoX3X6gQQoj7JqcrCyGEUI2EjBBCCNVIyAghhFCNhIwQQgjVSMgIIYRQjYSMEEII1UjICCGEUI2EjBBCCNVIyAghhFCNhIwQQgjVSMgIIYRQjYSMEEII1UjICCGEUI2EjBBCCNVIyAghhFCNhIwQQgjVSMgIIYRQjYSMEEII1UjICCGEUI2EjBBCCNVIyAghhFCNhIwQQgjVWGTIJCYm0qtXL5577jmWLl2qdTlCCPHAstW6AHNLT09n3rx5rF69mmrVqjFw4EA6dOjA448/rnVpQgjxwLG4kNm5cycdO3bEyckJgJ49e5KcnMzbb7/9j89VFAWAwsLCMvfVrmFn1jrVVlBQcHdPqF5LnUJUcrf9q2VXU6VKzO+uf3eAda2q8/u72/5Vr1G13qbutn/2NRxVqkQdf+9f6ftl6fvn31kpt7qnilq0aBG5ubmMHj0agBUrVnDw4EGmTZv2j8+9fv06x44dU7tEIYSwOO7u7tQq58NO1fqIcAfKy0wrK6s7em7NmjVxd3fHzs7ujp8jhBAPMkVRKCoqombN8rcWWFzI1KtXj7179xpvZ2RkoNPp7ui51tbW5SaxEEKIW6tevfot77O4o8s6d+7Mrl27uHr1Knl5eWzcuBFvb2+tyxJCiAeSRY5kRo8ezfDhwykqKqJ///48+eSTWpclhBAPJIvb8S+EEKLysLjNZUIIISoPCRkhhBCqkZARQgihGgkZIYQQqpGQEUIIoRoJGSHuU15entYliDu0b98+/vvf/1JYWMiePXu0LueBICFTwco7Yjw9PV2DStRh6f2LiooyuZ2SkoK/v79G1Yi78fXXXxMdHc2SJUvIyclh0qRJfPnll1qXZVbHjx9n79697Nmzx/ilNQmZCvb+++9TUlJivP3tt98SHBysYUXmZen9O3fuHDNnziQrK4vQ0FDmzJnDzJkztS7LLHx8fGjZsiUdOnSgffv2xp/79evHkSNHtC7vvq1Zs4Yvv/wSBwcH6taty8qVK1m1apXWZZnNlClTeO2115g/fz4LFixgwYIFfPzxx1qXZXln/Fd2Dz30EGPGjCEkJIQpU6ZQo0YNli1bpnVZZmPp/YuOjiYiIoIePXrwxhtvEBUVhZ1d1boMxK20a9cOX19fnn32WQC2bNlCcnIyw4YNY8qUKXz33XcaV3h/rK2tqVatmvG2vb09NjY2GlZkXjt27CA5Ofm284hpQUYyFWz8+PE8/vjj9O/fnxdeeIGvvvqKRx99VOuyzMZS+xcTE0NMTAyxsbE0aNAAR0dHfv/9dxYtWkRMTIzW5ZnF8ePHjQEDN0Y2R48epVWrVvd0jZvKpn379syaNYu8vDx+/PFHRo4cSceOHbUuy2waN258y2u6aElGMhVk3LhxJrfr1q1LfHy8ccboGTNmaFGW2Vh6/25mZWXFoEGDtC7D7GrXrs13331HUFAQJSUlJCYmUqdOHU6ePGmyCbSq+uCDD4iPj6d58+YkJCTg4+PDwIEDtS7LbOrUqYO/vz9t2rQxGbFp/b8nc5dVkDVr1tz2/qq+38LS+3ezq1evcuDAAQwGA56enjz88MNal2QW6enpREZGsmPHDmxsbOjSpQvjxo1jw4YNuLm5VdnZzNPS0m57f8OGDSuoEnXd6n9Q6/89CRkNHDt2jF9//ZXi4mI6dOhAy5YttS7JrCy5f9u2bWP8+PF4enpSUlLCb7/9RmRkJN26ddO6NHEL3bt3x8rK6pYXNNy8ebMGVamjMv7vSchUsISEBGJiYnj22WcpKSlh8+bNjBw5kv79+2tdmllYev/69u3L/Pnzady4MQDnz5/n7bffZu3atRpXdv+2bdtGdHQ0f/31l8kbsiW9CVuySvu/p4gKFRQUpFy9etV4+8qVK4q/v7+GFZmXpfcvMDCwTFtAQIAGlZjf888/r/z000/K+fPnlQsXLhi/LEV2drYyY8YMJTg4WBkwYIAyb948JTc3V+uyzKay/u/J0WUVrKSkhLp16xpvOzs7Y2VlpWFF5mXp/WvYsCFLlixBr9ej1+tZsmQJrq6uWpdlFnXr1qVbt240atQIV1dX45elmDBhAjY2NsyYMYOpU6eSk5PDxIkTtS7LbCrr/55sLqtg77//PnXr1jUOYVeuXMmff/7JnDlzNK7MPCy9f1euXGHatGns3r0bRVHo2LEjEyZMQKfTaV3afZszZw7FxcU8/fTT2NvbG9vbtWunYVXm07t37zKbNQMDA0lMTNSoIvOqrP97EjIVLD8/n48//tj4JtWhQwfeeustHB0dtS7NLCy9fwBFRUWcOnUKg8GAu7s7traWcSbAsGHDyrRZWVnxzTffaFCN+b377rv8+9//pk2bNgD88ccffPLJJ5XirHhzyM/PZ8GCBfzyyy+V6n9PQkYDV69eZf/+/ZSUlFjUIbClLLl/hw4dIjQ0FCcnJ0pKSsjKyuKTTz6hdevWWpcmbqH06LKCggKuXLlC06ZNsba25tSpU7i5uZGUlKR1iRZNQqaCWfohsJbev4EDBzJu3DhjqOzfv5/p06ezcuVKjSu7dxMnTmTatGkMGzas3G34VX0kc/HixdveX9X3OwUHB7NmzRpatGhh8vtTFAUrKyvN552zjHF+FTJv3jyWLVtW5hBYS3kTtvT+5ebmmoxaPD09q/yUKy+++CIAo0aN0rgSdRw7doxu3bqRkJBQ7v1VPWRKT8L8448/ytxXWFhY0eWUIUeXVbDi4mLjGzDcmG/IEqbsKGXp/atTpw4//vij8faPP/6Ik5OTdgWZgYeHB3Bjbq86depgZWVl8lXVHTp0CIBffvml3C9LUfphoVRJSQn9+vXTqJr/IyOZClZ6COzNR4BU9U9SN7P0/k2bNo2wsDAmTJiAoig88sgjmh+9Yy5jxozh8OHDJkfKWcKO/3feeQeAgIAAunTpYnLfxo0btSjJrIYPH86vv/4KQIsWLYzttra2dO/eXauyjGSfTAWz5ENgwfL7Vyo3N5eSkhLNj9wxJz8/P9atW2dR098DJCUlUVhYyIIFC4yBAzdG3YsWLWLTpk0aVmc+06dPJyIiQusyypCQ0cj169exs7OrdNd+MBdL7d/ly5eZPn06e/bswdbWlk6dOjF+/HicnZ21Lu2+hYeHExISQtOmTbUuxazi4+P57bff+Omnn0w+2dvY2NC5c2d69eqlYXXmU1BQwNatW8nJyQHAYDBw4cIFQkNDNa1LQqaCHT16lPDwcOPMsE2bNmXWrFk88sgjGldmHpbev5dffplnn32WoKAg4MbmwF27drFo0SKNK7t/CQkJjB8/Hp1Oh42NjfHoJEuZuywuLq7cc4EsRUhICHl5eZw7dw4vLy/27NmDp6cnCxYs0LawCpzCRiiK8uKLLyopKSnG2xs3blSGDBmiYUXmZen96927d5m2oKCgii9EBc8884zy66+/msxbZklzl1WGebzU9OyzzyolJSXKtGnTlN9//125fPmy8uKLL2pdliI7/itYQUEBPj4+xtvPPfccn3zyiYYVmZel9+/JJ5/khx9+wN/fH4Cff/7ZeHRWVVe3bl28vLws4oiy8tSvX5/hw4fTunVrk2lz3n77bQ2rMp+HHnoIKysrmjRpwtGjR+nTp0+lOIRZQqaClG4+atGiBZ999hn9+/fHxsaGxMREvLy8NK7u/ll6/0pPdFMUhfj4eCIiIrC2tiYnJ4c6deoQGRmpdYn3rUWLFrzwwgt07twZOzs7Y7ulvAl7enpqXYKqmjVrxrRp0xg0aBDvv/8+GRkZFBUVaV2W7JOpKJZ+4SRL79/tFBYWmlzutqqKiYkpt91SQgZuHBV47tw53N3dyc/Pp0aNGlqXZDYGg4HffvsNLy8vNm/ezK5du3jxxRdp1qyZtoVpurFOiCrmhRdeMLltMBgs5noyf1dSUqKcO3dO6zLMZufOncqzzz6rdOvWTUlPT1c6dOigbNu2TeuyzGbq1Kll2j744AMNKjElm8sq2KlTp1i2bBm5ubkoikJJSQkXLlxg6dKlWpdmFn/99Rdz5szh3LlzzJ8/n9mzZzNu3Dhq166tdWn3pbKf8GYO3377LXPnziUvL8/Y1qhRI4s5j2Tu3LksW7aM1157DZ1Ox7fffsuYMWPo2rWr1qXdlwkTJnD+/HlSU1M5fvy4sd1gMJCdna1hZTdIyFSw0aNH06NHD/bt20dwcDBbt27VfjhrRhMnTqRLly4cPHiQmjVrotPpeP/99/nss8+0Lu2+lJ71XllPeDOHxYsXs3btWqKjoxk9ejS//vorO3bs0LossykpKcHFxcV4+/HHH9ewGvMZOXIkFy9eJDIy0mTTpo2NDY899piGld0gc5dVsJKSEt555x2efvppWrVqRWxsLAcPHtS6LLO5cOECL774ItbW1lSrVo3Ro0dz+fJlrcsym4iICBITE5k3bx55eXm3nHSxKnrooYdo3LgxzZs359ixY/Tt25fTp09rXZbZ1K9fn59//hkrKyuys7P59NNPadiwodZl3bdGjRrRoUMHvv/+ex599FHat2+PtbU1f/zxR6XYVyghU8EcHBwoLCzk0Ucf5fDhw1SrVq3Kz+J7MxsbG65fv248DPbMmTNYW1vOn1lUVBRbtmxh48aNFBcXs2rVKmbOnKl1WWbh4ODA7t27ad68OT///DOZmZmVYnOLuUydOpXExEQuXbrEc889x5EjR5g6darWZZnN5MmT+fTTTzlx4gTvvfcehw8fZuzYsVqXJTv+K1pcXJwyYsQI5cqVK8qzzz6rvPLKK8qIESO0LststmzZovTu3Vtp3769MnLkSKVTp07Kzz//rHVZZtO7d2+lpKTEeFJmUVGR4ufnp21RZnLs2DHlo48+UkpKSpRRo0Ypbdu2Vb766iutyzKbTZs2KYWFhVqXoZrg4GClpKREWbBggbJgwQJFURSlb9++GlclO/4rnJeXF3369MHR0ZG4uDgOHTpUZmbYqszb2xsPDw8OHjyIwWBg6tSpVX6n/81KR2WlI7XCwkKLGKktW7YMFxcXxo0bx4ABA7hy5QrOzs4Wcx0ggO+//56pU6fyzDPPEBQUZBHnb93MYDBQUlLC5s2bmTJlCnl5eSYHcWil6v93VDGjR482ztxbv359nnvuOYs6Vv/FF1/E2dmZZ555hh49euDs7FwprmlhLr6+vrz77rv89ddfLFmyhKFDhxIQEKB1Wfdl0aJFbNy40bgjPD8/n7i4OP79739bxJxspRYsWEBSUhJPPfUUn3/+Ob6+vkRHR2tdltn06dOHrl274urqSuvWrenbt2+Za8xoQUYyFezxxx8nJiaG1q1bm8xQ3K5dOw2run+Wfohv6YwGAQEB1K5dG0VR2LdvH/369avyn/YTEhJYuXIlNWvWBG7sV3N1dWXQoEEEBgZqXJ15OTo60rZtWy5fvsylS5fYv3+/1iWZzeDBgxk+fLjxUg1Lly4lPT1d46rkjP8KV94ssJZwYahSlnqI761mNMjMzKS4uFjz66jfj4CAANatW2e8vWHDBnr27An83/XjLcHixYv54YcfKCwsJCgoiMDAQOrXr691WWYzfPhwPv/8c+zt7cnPz2f+/PkkJiayfft2TeuSkUwFmzRpUpnzYizp09T48eNZtmwZu3fvpri4mI4dOzJ06NAqv9/ip59+Mrmdk5PDrFmz2L59O9OmTdOoKvMoKSlBr9cbN+OWBsz169e1LMvsMjIymD59Oi1bttS6FFX06NGDV199leHDhzNr1iw6dOhg8uFBKzKSqSD79u2jpKSEiIgIIiMjjZ+Ii4uL+fDDD9mwYYPGFZrHrFmzOHv2LP369UNRFFavXo2rqysTJkzQujSz2bVrFxEREXTp0oUPPvigyl8d89NPPyU1NZVZs2YZ+5KTk0N4eDhPPfUUI0aM0LhC8ygqKmLHjh38+eefJu19+vTRpB41rF69mkmTJvHxxx9Xms24EjIV5OOPP+bXX38lNTXVZGp4W1tbnn76aV5++WUNqzOfoKAgEhISjCOX4uJiAgMDWb9+vcaV3b/c3FxmzpxpHL1YylGBBoOBDz/8kHXr1vHYY49hZWXFiRMn6N27Nx9++KHW5ZlNaGgoaWlpxj6WmjFjhoZV3b9hw4YZ+6MoCsePH8fR0dF4oqnWm+Jlc1kFGTVqFHBjJ6slfXL6O4PBQHFxsfFMY4PBYBHXjL959JKYmGjcSW4JbGxsmDZtGm+//bZx9ol//etfFnE2/M2OHj3K+vXrLe56OaXvLcXFxdjaVr63dBnJVJBx48bd9v6q/mmq1MKFC0lJSTFe1OuHH37Ax8eHkSNHalzZ/WnRogW2trbodDqTNynFwi5RbMneeustJk+ejE6n07oUVVTWgzQqX+xZqPbt25f7CcpSMr70EN+goCBq167NL7/8gqIoBAYGVpptw/dDQqTqy8/Px9fXF3d3d5M5vbTenGQuDz30EHv37uXJJ5+sFHOWlZKRTAW5+dwRuHHYcp06dejcuTOTJk3CyclJm8LMxJIP8RWWofQ8rr9r3759BVeijo4dO5Y5qMHKykrz/z0JGQ1duXKF5cuXc+LECebOnat1OWb190N8LWUnuajatmzZYjy8vkOHDjz77LNal2TxqvbJC1XcQw89xJtvvsnRo0e1LsWsdu3aRVBQEHBjvigJGFEZfP7558TExNCgQQMaNWrEwoULWbhwodZlmU1eXh5z5syhb9++9O7dmxkzZpCbm6t1WTKSqQz69OljEdclsdRDfIVlCAwMZMWKFcbpnPLy8ujbt69FHF4PNw4ucnBw4IUXXgAgPj6e69evM2fOHE3rkh3/Gtu4cWOV3x8Dln2Ir7AMiqKYzBdob29fKQ/5vVeHDx/m+++/N96eNGkSvXr10rCiGyznFa7kSneM30yv1+Pm5qb5Jw1zGDFiBLa2tmzfvt3kkr1yiK+oLDp27MioUaMIDg4Gbpyz1qFDB42rMh9FUcjOzjZeWiM7O7tSnKMmm8sqyMWLF01uW1tbU7t2bYv5xP/3/v2dq6trBVUiRPkUReG///0vu3fvRlEUOnbsyIsvvljlRzMXL17E1dWV1atXs2jRIrp164aiKPz888+EhITQv39/TeuTkBFCWDyDwUBhYSEODg4AnDhxAjc3N+zs7DSu7P49/fTT1KhRgy5duvDwww9Tq1YtrKysaNeuHc2bN9e6PDm6TAhh2c6fP4+fnx/btm0zti1ZsoSAgAAuXLigYWXmsW3bNj7//HM8PDw4f/48y5YtIykpiU2bNnHgwAGty5ORjBDCsr3xxhv4+/uXuQDbqlWr2Lx5M7GxsRpVpo7s7Gw2b97M4sWLOX36NKmpqZrWIyEjhLBotztFIDAwkMTExIotyMyKi4vZt28f27ZtY/v27eTn59O5c2c6d+5Mx44dNb8URdXe4yWEEP+guLhY6xJU1a5dO9q0aYOvry8xMTE0atRI65JMyD4ZIYRFa9myJStWrCjTvmrVKho3bqxBReY1cOBArl69yqpVq1i9erXxAomVhWwuE0JYtMzMTIYOHUq9evVo3bo1iqJw6NAh0tLS+OqrryrdJ/97lZ6ezo4dO9i+fTuHDh3C3d2drl27MmjQIE3rkpARQli8vLw8fvjhB44cOYKVlRUeHh74+flhb2+vdWlmVVhYyKFDh/jf//7H2rVr+fPPP9m+fbumNUnICCFEFfbjjz/y22+/sW/fPi5cuEDr1q3p1KkTHTp0oFmzZlqXJyEjhBBV2csvv0zHjh3p2LEjHh4eWFtXrl3tEjJCCCFUU7kiTwghhEWRkBFCCKEaORlTCGHRAgICyMvLK9Mul6GoGLJPRghh0Y4ePcprr73G3LlzadCgQZn75TIU6pKQEUJYvISEBH766ScWLFigdSkPHAkZIcQDQa/Xaz5Z5INIQkYIIYRq5OgyIYQQqpGQEUIIoRoJGSGEEKqR82SEEA+E1atXM2vWLLKzs4H/O0/myJEjGldm2WTHvxDigdCjRw8+/fRT3N3dtS7lgSKby4QQD4R69epJwGhARjJCiAdCZGQk6enpdOnSxeRiZX369NGuqAeA7JMRQjwQ9Ho9NWvWZP/+/SbtEjLqkpGMEEII1chIRghh0V5//XUWLVpE9+7dsbKyKnO/zMKsLhnJCCEsWkZGBjqdjosXL5Z7v8zCrC4JGSGEEKqRQ5iFEEKoRkJGCCGEaiRkhBAW78cffyQuLo5z586ZtC9fvlyjih4cEjJCCIsWFRXFt99+y5kzZxg4cCBr16413vfdd99pWNmDQQ5hFkJYtC1btrBmzRpsbW0ZNmwYL7/8MtWqVcPPzw857kl9EjJCCItWOtsywKOPPsqiRYsYMWIEzs7O5Z43I8xLNpcJISyar68vw4YN4+DBgwA0a9aM+fPn8+6775bZRyPMT86TEUJYvF27dqHT6XjssceMbZcuXWLx4sVMmDBBw8osn4SMEEII1cjmMiGEEKqRkBFCCKEaObpMiHswffp09uzZA8DJkydxdXWlevXqwI0T/Ep/vh8XLlygR48eTJ8+nQEDBhjbv/zyS44fP87MmTPvex1CqE1CRoh7EBERYfy5e/fuREVF8cQTT5h9PdbW1syaNQsvLy+aNGli9uULoTYJGSHMJC8vD29vb+Lj442BMGLECIYMGcKPP/6IlZUVJ0+e5OrVq3Tp0oWIiAjs7Ow4efIkkZGR/PnnnxgMBoYNG0b//v0BqF69OiNGjOC9997ju+++o1q1aibrPH36NFOnTiU3N5eMjAxatGhBdHQ09vb2PPHEE7z00kukpKSg1+sJCwsjOTmZY8eOodPpWLhwITVq1Ljt+oW4X7JPRggzcXBwoE+fPqxYsQKAc+fOcfr0abp16wbAH3/8wVdffUVSUhInT55k+fLlFBcX88477/Dee++xevVqvv32WxYvXmxyieCRI0fi4ODAvHnzyqwzPj6ePn36sHz5cjZu3MiFCxdISUkBoLCwEBcXFxITExk0aBARERFMmDCBpKQk9Ho9mzdvvqP1C3E/ZCQjhBkNHjyYoUOHMnr0aJYvX07//v2xsbEBIDg4mJo1awLQu3dvNm/eTMeOHTl37hzjx483LiM/P5/ff/8db29v4MYmszlz5hAcHEzXrl1N1hcWFsaOHTv4/PPPOXPmDBkZGeTm5hrv79mzJwCPPPII7u7u1KtXD4BGjRrx119/cebMmVuu39PT0/wvkHjgSMgIYUZNmjShefPmbN68mcTEROOoBjCGDdyY6sTa2hqDwUDt2rVNJm3MysqiVq1aZGZmGtsaNmzIhx9+yNixY+nTp4+xfcyYMRgMBvz8/HjmmWe4dOmSyXxcdnZ25f5c6nbrF8IcZHOZEGY2ePBgZs+eTevWrY0jB4D169dTWFhIQUEBa9asoVu3bjRp0gR7e3vjm/ylS5cICAggNTW1zHL9/Pzw9vbm66+/NrZt376dt956i169emFlZcWBAwcwGAx3XOvdrF+IeyEjGSHMrFu3bkRERDBw4ECT9urVqzN48GCys7Pp2bMn/fr1w9ramtjYWCIjI/niiy8oLi4mNDSUtm3bcuHChTLLjoiIYN++fcbbo0eP5q233qJOnTo4ODjQrl27u5qPq1q1ardcvxDmINPKCGFm//vf/5g4cSLr1q0zzvIbHh5Os2bNeOWVVzSuToiKJSMZIcxo7Nix/Prrr8yaNUumkRcCGckIIYRQkez4F0IIoRoJGSGEEKqRkBFCCKEaCRkhhBCqkZARQgihGgkZIYQQqvl/5mVvVXDGf+oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['TypeName'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title('Laptop Type-Based Pricing',fontsize=14)\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4164,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+pKenk5GRQUxMDJs2bcLb25ucnBx++eUXnn/+ebp06QLA9OnTadiwIdnZ2WRkZPDCCy/QvXt37rrrLv7880+SkpLo06cPzzzzDACffPIJS5cuxc/Pj/bt27Np0yZ++eWXEl9/fn4+OTk5GAwG+7EBAwbg7++PxWJBo9GwdOlSPv/8c9RqNSEhIbzxxhskJCTw6quvotfrMRgMLF26lN9//50PPvgAi8WCj48Pzz77rL1C9fz581m/fj1Wq5Xq1aszY8YMIiMjGT16NK1bt2b37t0kJSXRrl073njjjSvWq6SkpGAymSgsLESr1RIQEMDs2bPt+4+NHj2akSNHotFoeP/99+3XJSQk0LNnT9588012797NW2+9RX5+PiqVikcffZTbb7+dlJQUnn32WTIyMoCiXeafeOKJyvorJlyE1NcSwvGqZEKTs3gd2d/8WOp7FouFzEoYGQi8tx8Bw3pf85zdu3fTvHlzezJzuVtuuYVt27Zd8/rz58/zxx9/sGjRInx8fPjxxx9577336NWrFwB///03K1aswN/fn4kTJ7J48WIee+wxNm3aRMOGDRk5ciTz5s0DYNy4cRw/fpyGDRvy4IMPEhkZyffff0+XLl3Izc1l06ZNPPPMM3zxxRclYjAYDHzzzTckJyfTq1cvRowYwenTp1m2bBlLly4lICDgqoUUg4KCePrppxk3bhzh4eG0bduWDh060K9fP3Q6HUeOHOGtt95i+fLlREdHs3DhQubPn0+/fv04duwYGzdupHr16pw+fZp33nmHjz76iOrVq3Ps2DHGjh3L+vXrWb9+PUePHuX7779Hq9WyePFipk+fzoIFC4CipOOrr77CYDDQp08f/vrrLzp27FgizrFjx/Lwww/TsWNHWrVqRdu2bbnzzjtp2rRpifN69epl/+x/+eUXXn/9dZ599lmysrKYOnUqn376KTVq1CA5OZmhQ4cSGxvL8uXLqVGjBp999hkGg4Fp06aRk5NT6k6ywn0Vb84rhHCcKpnQuIur7UBerHr16rzxxhusXr2aM2fOsG/fvhKP9t588832KaumTZuSlZVV5r4HDx7MBx98QHp6OuvWraNbt24EBgZecV6PHj0AiIyMJCwsjKysLLZs2ULv3r3t548cOZLt27eX2s/YsWO555572LlzJzt37mTBggUsWLCApUuX8ueff9KlSxeio6MBGDNmDAA7duwgOjqa6tWrA7Bt2zYuXrzIxIkT7aMrKpWKhIQEfv31Vw4cOMCQIUOAos80Pz/f3v/tt9+OWq3G39+f2rVrl/oZRUVFsWzZMo4fP86OHTvYsWMHw4YNY8qUKYwcOfKK8/fu3cuLL77I559/Tnh4OFu2bCElJYX/+7//s5+jUqmIj4/n1ltvZfz48SQlJdG5c2eeeuopSWY80OLFiwEYNmyYwpEI4bmqZEITMKz3VUdPnFmcrW3btixYsID8/Hx8fX0pLCwkLy+PkJAQtm/fTuvWre3n2mw2+5+Ld+79559/ePjhhxkzZgy33HILN910EzNnzrSfd/mi3uLFrGUVGBhI7969WbVqFatXr2bGjBmlnnf5zubFfWi12hJ9XW0tzK5du9izZw/jxo3j9ttv5/bbb+fJJ5+kf//+bNu2DY1GU+Jx+4KCAs6fPw+AXq+3H7darXTq1IlXX33Vfu+SkpKIiIjAarUybtw47r33XqDos7s8aSnLZzR79mzuueceGjRoQIMGDRg5ciQrV65kwYIFVyQ0p06d4tFHH+Wtt96ifv36QNGoX/369fn+++/t5yUnJxMaGoqXlxebNm3izz//ZPv27dxzzz188MEHtG3bttTPTLin+fPnA5LQCOFIVTKhcRUtW7akQ4cOTJkyhZdeeonU1FQeffRR6tevT3x8PLNmzQIgNDSUgwcP0rJlS/bu3UtKSgoAO3fupHnz5owdOxaLxcLMmTOxWCzX7Vej0WA2m6973siRI3n44YcJDg6mZcuWZf66unbtyksvvcS4ceMICAhg6dKlpZ4XGhrK/Pnzad26Ne3btweK1qvk5+fTqFEj6tatyyeffMLFixeJiIjgu+++Y/v27YwdO7ZEOx07duS9997j1KlTNG/enC1btjB58mS2bNlCly5dWLx4sX1tzty5czl06BCff/55mb+e9PR05s6dy2uvvYavry82m41Tp05dMeWUkpLCQw89xDPPPEOHDh3sx1u3bs2ZM2fYuXMnN910E4cPH2bEiBH8+OOPfPvtt9hsNp5++ml69OhBfHw8p0+floTGw723Lx3DdUZgXZFereaxVqFKhyFEqSShUdjs2bP57LPPGDVqlL3MvUajwc/Pj02bNnHXXXcxefJkXnzxRRYvXkyzZs3sTwTFxcWxfv16+vbti5eXF506dSIrK4vc3Nxr9nnbbbfx8ssvXze2xo0bExQUxPDhw8v1NXXq1ImhQ4cybNgwfHx8aNiwYanrhOrWrcsHH3zAO++8w4ULF/D29iYgIICXXnqJevXqAdjX2ABUq1aNWbNmcfr06RLtNGzYkJdeeompU6eiUqnQarXMnz8fvV7PPffcY1+zolKpiI6O5vXXXy/X1zNjxgzeeecdBgwYgE6nw2w207FjR1544YUS582bN4+0tDQWLlzIf//7XwAiIiJYsGAB7733HrNnz8ZoNGKz2Zg9ezbVq1fn/vvvZ8qUKcTFxaHT6YiNjSUuLq5c8Qn3Y7BaKVgZrnQY5TcwVekIhLgqla088xAuzGg0cvDgQZo3b15iGgTg8OHDNGnSpEztuMp+QNnZ2Rw8eFDR2iYJCQmMHj2adevWlZqQXM2BAwfYs2cP9913HwCff/45+/bt491333VQpEVc5d45Snn+HrubXbt20a5dO6XDcJhu3boBsHnzZgBe35PqlgmNz8BUprQpGben3ztP5m737lo/50FGaFxWYGCgosnM3LlzWbJkCdOmTStXMgNFIy8LFixgyZIl9lGRsowICSGEEBUlCY0o1eOPP87jjz9eoWv9/f157733KjkiIdzX1daRCSEqjyQ0QgjhYOHh7je9JIS7qTKbU3rIUiFRRcnfX/e2cOFCFi5cqHQYQni0KpHQaDQaTCaT0mEIUWH5+fl4eXkpHYaoIKUTmuC0E9Q4uVWx/oVwhiqR0AQHB5OcnHzdyrtCuBqbzYbBYOD8+fNEREQoHY5wMyqrhea7vqLvknHc/tNzxJzZoXRIQjhMlVhDEx4ezrlz54iPj7/uuYWFheh0OidEJSqbp947Ly8vIiMjS916Qohrue3nF6h18jfO1O9GUMYZOv0yi9UjvqTQJ0jp0ISodFUioVGr1dSqVatM5+7atYtWrVo5OCLhCHLvhLgkIPMstU7+xsG2I9nTcQIhqcfps3Q8HTe/yW93vgyXbSsihCeoElNOQghR1dQ9uhEbKuJbDAaVioxqDdl384PUPrGFqPO7lQ5PiEonCY0QQjjY2rVrWbt2rfM6tNmoe3Q9F6q3weB/ae1VfMshWDQ6apze5rxYhHASSWiEEMLB9Hp9iR3iHS08+RCBWec4FXtHieNmL1+SY1rJ4mDhkSShEUIIB/vwww/58MMPndZf3aPrMWt0JNTresV752t3JCgzAf/sRKfFI4QzSEIjhBAOtmTJEpYsWeKUvlQWM3WObeJcnVsweftf8X5irY4AMkojPI4kNEII4UHCkw/hU5DFmYbdS30/O7gmOYHRVE/Y7uTIhHAsSWiEEMKDhF88BMDFqBaln6BSkVirI1HndqM2G50YmRCOJQmNEEJ4kLCLR8j1j6TAL+yq55yv3QGtuYDIpP1OjEwIx5KERgghPEh48mHSIptc85zk6m2xaHREJ/zlpKiEcLwqUSlYCCGUtHnzZqf0452fSUB2IkebDbzmeWYvXzLC6hOWctQpcQnhDDJCI4QQHiLs4hEAUq8zQgOQEd6AkNTjYLM5OiwhnEISGiGEcLC33nqLt956y+H9hCcfxoaK9Gqx1z03PbwB3sZs9HkpDo9LCGeQhEYIIRxszZo1rFmzxuH9hF08RFZoHcy661clzghrAEBI6jFHhyWEU0hCI4QQnsBmIzz5MKkR159uAsgMrw9ASOoJR0YlhNNIQiOEEB7APycJn4KsMq2fATDp/MgJjClaRyOEB5CERgghPEBY8mGA6z6yfbmM8AaEypST8BCS0AghhIP5+vri6+vr0D5CU49hUWvJDK1X5msywhoQkHUerSnfgZEJ4RxSh0YIIRzsp59+cngfgRkJ5ARVx6rxKvM16eENUGEjOO0kqVHNHBidEI4nIzRCCOEBgjITyA6uVa5rMsLlSSfhOSShEUIIB3v55Zd5+eWXHda+ymrGP+s82SHlS2jyAqIo1PnLwmDhERya0MTHxzN69GgGDRrE4MGDOXjwIAAfffQRvXv3plevXsybNw/bv5Uq09PTGTduHH379iUuLo7du3c7MjwhhHCKTZs2sWnTJoe175+dhMZqJqucIzSoVGSE1SdUEhrhARyW0OTn5/Pggw8ybtw4VqxYwcMPP8zkyZPZsmUL69atY9myZaxZs4YdO3bY55dnzpxJ+/btWbt2LW+++SaPP/44+fmyWE0IIa4lMDMBoNwjNACZYfUISj8lWyAIt+ewhGbbtm3UrFmTrl27AtCjRw/effddNmzYQFxcHHq9Hm9vbwYPHsyqVaswm81s3ryZoUOHAtCkSRPq1KnD1q1bHRWiEEJ4hKCMfxOa8o7QANnBNdCZDHgXZFZyVEI4l8MSmlOnTlGtWjWee+45Bg8ezNixY7FYLCQlJREdHW0/LyoqiuTkZDIyMrBarYSGhtrfi4yM5MKFC44KUQghPEJgZgIFvsEU+gSW+9qcoOoABGSdr+ywhHAqhz22bTab2bJlC19++SWtWrVi48aNjB8/nnr1rqyRoFarsVqtpbaj0WjK1W/xOp0bsWvXrhtuQyhD7p378uR7p1YX/e5o/xrVtUlMTKy09n2Sj5Oij6pQmwVGb7oDltMHSbSGXvPceuhKvU+efO88nSfdO4clNBEREdSrV49WrVoB0LNnT6ZPn45arSYl5dLursnJyURFRREWFgZAVlYWQUFB9vciIyPL1W/z5s3x9vaucNy7du2iXbt2Fb5eKEfunfvy9Hv3yy+/lHi9YU8qMTExldZ+uCGZc3U6V6hNtSUc6yY1NVV5ZFz3+tQr7pOn3ztP5m73zmg0XnPQwmFTTrfddhvnz5+3d75z505UKhX3338/q1atwmAwUFhYyLJly+jZsydarZZu3bqxePFiAI4cOcKJEyfo0KGDo0IUQgi3pyvIwTc/g+yQ2hW63qrRYfCPJFCmnISbc9gITbVq1fjggw+YOXMm+fn56HQ65s2bR/v27Tl69Cj33HMPJpOJHj16MGjQIABmzJjB9OnTiYuLQ6VSMXv2bAICAhwVohBCOMXUqVMBeO211yq9bfsTThVYEFwsJ6g6AVnnKiskIRTh0K0PbrrpJr7//vsrjk+cOJGJEydecTw8PJyPPvrIkSEJIYTT/fnnnw5ruzihyQqpWeE2soOqU+f4L9c/UQgXJpWChRDCjQVmJGBRa8kNqPianJygGngbc9AVZFdiZEI4lyQ0QgjhxoIyE8gNqo5NU/EB90uPbsu0k3BfktAIIYQbC8g8R3ZQxaeboGiEBqQWjXBvDl1DI4QQAmrUqOGYhm02/HOSuFDzxh69zQ0qmq6SERrhziShEUIIB1u0aJFD2tUZs/Ey5ZMbEH39k6/BovUmzz9CRmiEW5MpJyGEcFP+2UVbw+QGRN1wWzlBNSShEW5NEhohhHCwJ554gieeeKLS2/XPSQIgL/DGRmigaGFwoEw5CTcmU05CCOFge/fudUi7fvYRmvJtEVOa7KDq+ORn4mXMxeTtf8PtCeFsMkIjhBBuyj8nCaO3PybvG6+obn/SKVumnYR7koRGCCHclH/OBfJucEFwsbzAonU4fjnJldKeEM4mCY0QQrgpv+wLlbIgGCDPv2jaShIa4a5kDY0QQjhYo0aNKr/RSqpBU8zoE4RZ6y0JjXBbktAIIYSDffLJJ5XeZmXVoLFTqcjzj8QvVxIa4Z5kykkIIdxQZdagKZYXECkjNMJtSUIjhBAONn78eMaPH1+pbVZmDZpief4RktAItyVTTkII4WBHjx6t9DYrswZNsbyAKHzz01GbjVi13pXWrhDOICM0QgjhhiqzBk2xvH+TI7/clEprUwhnkYRGCCHcUGXWoClmf3RbFgYLNyQJjRBCuKHKrEFTrHiERi/raIQbkjU0QgjhYK1bt67cBiu5Bk0xg381APwloRFuSBIaIYRwsHfffbdS27tUg6ZyR2isGh0GfZhMOQm3JFNOQgjhZoofrc6r5ISmqE2pRSPckyQ0QgjhYKNGjWLUqFGV1l7xU0gGv2qV1maxPP9I9DJCI9yQTDkJIYSDnTt3rlLb0+ddBC6tealMeQGR1Di9DWw2UKkqvX0hHEVGaIQQws3oc1OwqjUU+IZUetsG/0i0lkJ88jMrvW0hHEkSGiGEcDP63BQM+nBsak2lt11ceVimnYS7kYRGCCHcjD73okOmm+CyasGyMFi4GVlDI4QQDtapU6dKbU+fl0JGeMNKbbPYpe0PJKER7kUSGiGEcLDXXnut8hqz2fDLTeF87c6V1+ZlCr0DMWu9ZYRGuB2ZchJCCDeiM+aiNRc4bMoJlQqDXzX0eamOaV8IB5GERgghHGzIkCEMGTKkUtoqfmQ7zz+iUtorjcEvHF9JaISbkSknIYRwsLS0tEprS19cVM9RIzRAvl81wpMPOqx9IRxBRmiEEMKN6HP/LarngCrBxQz+4ejz0oqK6wnhJiShEUIIN6LPTcGGinx9mMP6MPhVQ2MpxLsgy2F9CFHZJKERQgg34peXQr4+FJvGcSsGDH7hALIwWLgVWUMjhBAO1qNHj0prq6ionuMWBAPk/5vQ+OamkBHewKF9CVFZJKERQggHe/755yutLX1uCtkhtSqtvdIUr8/RG2SERrgPmXISQgg3os9LceiCYIB8v6L1OfpcSWiE+5CERgghHKxPnz706dPnhtvxKsxDV5hHngMf2QawarzI9w1Bn5fi0H6EqEwy5SSEEA6Wn59fKe342mvQOHYNDRQX15OERrgPGaERQgg34Zfn+KJ6xfL9q8mUk3ArktAIIYSbuFRUzxkjNNVkykm4FYdOOb3++uusW7eOoKAgAOrWrcu7777LRx99xIoVK7BYLAwYMIBHHnkElUpFeno6zzzzDImJiajVal566SXatm3ryBCFEMJt+OYVbaFg8HNcUb1iBr8wfAqyUFsKsWp0Du9PiBvl0IRmz549vP322yWSki1btrBu3TqWLVuGRqPhwQcfpH79+vTt25eZM2fSvn17Jk6cyOHDhxk/fjzr16/H19fXkWEKIYRDxcXFVUo7voY0jN4BWLXeldLeteT/+ySVb14aeYHRDu9PiBvlsCmnwsJCDh06xGeffcaAAQN49NFHSUxMZMOGDcTFxaHX6/H29mbw4MGsWrUKs9nM5s2bGTp0KABNmjShTp06bN261VEhCiGEU0yePJnJkyffcDu+eWkO3fLgcvZaNDLtJNyEw0ZokpOT6dixI08++SR169bl008/5eGHHyYsLIxOnTrZz4uKiiI5OZmMjAysViuhoaH29yIjI7lw4UK5+j148MZ3iN21a9cNtyGUIffOfVWpe6euTWJiYrkv02Ymkan1r9C15VVoKNqY0nj2GIm2osrB9dCVep+q1L3zMJ507xyW0NSsWZMFCxbYXz/44IN8+OGHhISEXHGuWq3GarWW2o5GoylXv82bN8fbu+LDsbt27aJdu3YVvl4oR+6d+/L0e9etWzcANm/eDMCGPanExMSUu50gUw4Xw5tX6Nry0oX6A1BdZybb3l/qFffJ0++dJ3O3e2c0Gq85aOGwKacjR46wYsWKEsdsNhsxMTGkpFwawkxOTiYqKoqwsKJh1KysrBLvRUZGOipEIYRwHzYbvgbnTTkVegdg1ujwlQ0qhZtwWEKjVqt59dVXOXv2LADffPMNsbGx9OjRg1WrVmEwGCgsLGTZsmX07NkTrVZLt27dWLx4MVCUEJ04cYIOHTo4KkQhhHAbOmMuGkuhfVsCh1Op/n10WxIa4R4cNuXUqFEjpk+fzqRJk7BYLERFRfH2228TExPD0aNHueeeezCZTPTo0YNBgwYBMGPGDKZPn05cXBwqlYrZs2cTEBDgqBCFEMJt+P67UWS+Ptxpfeb7hcuiYOE2HPrY9sCBAxk4cOAVxydOnMjEiROvOB4eHs5HH33kyJCEEMItFdegcdoIDWDwDycs+YjT+hPiRsheTkII4WDF5ShuhK/h34TGSWtoivvyNaQ7rT8hboQkNEII4WAPP/zwDbdRnFg4c4QmXx+KlzkfbaEBs07vtH6FqAjZy0kIIRzMYDBgMBhuqA3fvDTMWh9MXs5LLIqTp+LRISFcmSQ0QgjhYH379qVv37431IavIQ2DPgxUqkqK6vqKFyBLQiPcgSQ0QgjhBnwNaRT4hV7/xEpUoC/qr3hBshCuTBIaIYRwA87cx6mYwT7lJAuDheuThEYIIdyAM6sEFyv0DsSi1sqUk3ALktAIIYSL05gK0BXmOfUJJwBUKgr0oTLlJNyCPLYthBAONmbMmBu6vniExODkERoorkUjCY1wfZLQCCGEg91wQqNAleBi+X5h+GcnOr1fIcpLppyEEMLBUlNTSU2t+CaPSlQJLpavD5MpJ+EWZIRGCCEc7O677wZg8+bNFbper+QIjT4Mn4IsVBaz0/sWojxkhEYIIVycjyENq1qD0SfI6X3bqwXny6PbwrVJQiOEEC7O/si2yvn/ZOdLcT3hJiShEUIIF6dEDZpixf1KcT3h6iShEUIIF1dUJdi52x4Ukw0qhbuQRcFCCOFgkyZNuqHr9YY0UiObVVI05VPgGwLIlJNwfZLQCCGEgw0bNqzC16osZnzyMxV5wgnAqvGiwCcIHxmhES5OppyEEMLBzp49y9mzZyt0bfHTRUqtoYGiaSe9JDTCxckIjRBCONjo0aOBitWhUbJKcDEprifcgYzQCCGEC1OySnCxfH0YPvKUk3BxktAIIYQLc4kRGr+wose2bTbFYhDieiShEUIIF+ZrSMeGinxfZR7bhqLiehqrCV1OjmIxCHE9ktAIIYQL8zWkYfQNwqZRbslj8XSXT3qmYjEIcT2yKFgIIRzsqaeeqvC1vnlpGBRcPwOXprt80jMUjUOIa5GERgghHKx///4VvtbXkKbo+hm4tJ+TJDTClcmUkxBCOFh8fDzx8fEVutbXkEaBQtseFMvXhwOS0AjXJiM0QgjhYBMmTAAqUIfGZlV0Y8piZp0ek9YXX0lohAuTERohhHBR3gVZqK0W+wiJkvL1oTJCI1yaJDRCCOGiimvQGBReQwNFC4N9MjKVDkOIq5KERgghXJQrVAkulq8PwydNRmiE65KERgghXJQrVAkuVqAPlTU0wqXJomAhhHCw6dOnV+g6vSuN0PiF4WUwYDUUoNb7KB2OEFeQhEYIIRysZ8+eFbrOx5BOoc4Pi5fyCURxcT/LxXTUdWIUjkaIK8mUkxBCONjevXvZu3dvua/zzVP+ke1iBf/GYU5OVTgSIUonIzRCCOFgTzzxBFD+OjSuUCW4WHEclovpCkciROlkhEYIIVxU0QiNslWCixWPFFmS0xSORIjSSUIjhBCuyGZD7wJVgosV+AZhVasxS0IjXJQkNEII4YK8TAa05gKXSWhQqTGGBMuUk3BZktAIIYQLcqUaNMXyQ0Nkykm4LFkULIQQDjZr1qxyX+NKVYKLFYQGy5STcFkOH6HZuHEjbdu2tb/+6KOP6N27N7169WLevHnYbDYA0tPTGTduHH379iUuLo7du3c7OjQhhHCKzp0707lz53Jd44ojNAWhIVguSkIjXJNDE5rTp0/zxhtv2JOWLVu2sG7dOpYtW8aaNWvYsWMHP/30EwAzZ86kffv2rF27ljfffJPHH3+c/Px8R4YnhBBO8ccff/DHH3+U6xpfQ1G9F1fYabtYQWgIltRMbBaL0qEIcQWHJTT5+fk8/fTTTJkyxX5sw4YNxMXFodfr8fb2ZvDgwaxatQqz2czmzZsZOnQoAE2aNKFOnTps3brVUeEJIYTTPPfcczz33HPlusbXkI5Fo6PQ299BUZVfQUgIWK1YUmRPJ+F6HJbQvPDCCwwbNozY2Fj7saSkJKKjo+2vo6KiSE5OJiMjA6vVSmjopXoLkZGRXLhwwVHhCSGES7NXCVaplA7FLj80BJDiesI1OWRR8Ndff41Wq+Xuu+/m3Llz9uPFU0+XU6vVWK3WUtvRaDTl7vvgwYPlvuZ/7dq164bbEMqQe+e+PPne5eTkAJd9jeraJCYmXvMadUYiWV4B1z3PmWIaFSU0R7btwGzKsR/35Hvn6Tzp3jkkoVm+fDkFBQUMHDgQk8lk/3PTpk1JSUmxn5ecnExUVBRhYUWL3rKysggKCrK/FxkZWe6+mzdvjre3d4Vj37VrF+3atavw9UI5cu/cl6ffu4CAAAD717hhTyoxMdfe4DHYnEtWSO3rnudMBSGZANQNCCHw36/F0++dJ3O3e2c0Gq85aOGQKaelS5eyZs0aVq5cySeffIKPjw8rV66kV69erFq1CoPBQGFhIcuWLaNnz55otVq6devG4sWLAThy5AgnTpygQ4cOjghPCCFcnq8hjQIX2fagWEFoMCDbHwjX5NQ6NN27d+fo0aPcc889mEwmevTowaBBgwCYMWMG06dPJy4uDpVKxezZs+2/1QghhDt79913y3W+2mzE25jjUo9sA1h1OtTBAZhlDY1wQQ5PaGrUqMGePXvsrydOnMjEiROvOC88PJyPPvrI0eEIIYTTtW7dulzn+xqKEgZXKqpXTBMRKrVohEuSrQ+EEMLBNm7cyMaNG8t8fnGVYIMLJjTayDCZchIuSbY+EEIIB3vllVcA6NmzZ5nOd8UqwcU0kWEU7Lzxp0mFqGwyQiOEEC7GFfdxKqaJCMNyMb3UMhxCKEkSGiGEcDH6vDSsKjVG32ClQ7mCNjIMW74Ra06e0qEIUYIkNEII4WJ8DGkU+IZgU5e/uKijaSKKHiWXdTTC1UhCI4QQLsbXkOaS62egaA0NyPYHwvWUKaEpbVO1Rx99tNKDEUIIT/Txxx/z8ccfl/l8+z5OLkj7b0JjlhEa4WKu+ZTTjBkzSE5OZteuXaSnX8rGzWYzJ0+edHhwQgjhCS7fpLcsfA1ppEeU7xpn0UQUj9BIQiNcyzUTmrvvvptjx44RHx/PnXfeaT+u0Who06aNw4MTQghPsHr1agD69+9/3XNVVgs++ZkuO0KjDvJH5a2TNTTC5VwzoWnRogUtWrSgc+fOREVFOSsmIYTwKHPmzAHKltD45GegtlldNqFRqVRoIsNkykm4nDIV1ktISODpp58mKyurRO2B4t86hBBCVA57DRoXXRQMxdsfyKJg4VrKlNC89NJLDBkyhKZNm6JSqRwdkxBCVFn2KsEuOkIDRQuDC0+cVToMIUooU0Lj5eXF2LFjHR2LEEJUea5cJbiYJiIUyx97lQ5DiBLK9Nh2w4YNiY+Pd3QsQghR5V3axylU4UiuThMZhjUjG5uxUOlQhLAr0wjN2bNnGTJkCDExMXh7e9uPyxoaIYS4vq+++qrM5/oa0jF6B2LV6BwY0Y3R/vvotjklQ+FIhLikTAnNf/7zH0fHIYQQHqtmzZplPteVqwQX00QWb3+QqnAkQlxSpoSmUaNGjo5DCCE81uLFiwEYNmzYdc915SrBxbSR4cC/2x9E6BWORogiZUpoOnbsiEqlwmaz2Z9yqlatGr/99ptDgxNCCE8wf/58oIwJjSGN5JhWjg7phmgu3/5AEhrhIsqU0Bw5csT+Z5PJxPr160scE0IIUQlsNnzz0ijQu+6CYABNeDCoVP/Woin7dJoQjlTu3ba9vLzo168f27Ztc0Q8QghRZemMOWisJpefclJptWjCg2X7A+FSyjRCk5mZaf+zzWbj4MGDZGdnOyomIYSoki49su3aCQ0U1aKR7Q+EKyn3GhqAsLAwpk2b5tDAhBCiqikuqmdw8REaKNp1W7Y/EK6k3GtohBBClM/SpUvLdJ6voegxaHcYodFGhmGIP610GELYlSmhsVqtfPrpp/z222+YzWZuueUWJk6ciFZbpsuFEKJKCw8PL9N5l/ZxKtv5SiraoDINrFalQxECKOOi4Dlz5rB9+3buv/9+xo4dy549e5g9e7ajYxNCCI+wcOFCFi5ceN3z9IY0TFpfzDrXfxRaExkOZguqHIPSoQgBlHGEZuvWrfzwww94eXkB0K1bNwYMGMBzzz3n0OCEEMITFCczY8aMueZ5PoZ0t5hugqIpJwBVujwgIlxDmUZobDabPZkB0Ol0JV4LIYS4ce5QJbiYJqKoVo46I0fhSIQoUqaEpnHjxsyaNYuEhAQSEhKYNWuWbIcghBCVzB32cSp2aYQmS+FIhChSpoRmxowZZGdnM3z4cIYOHUpGRgbPP/+8o2MTQogqxS1HaGTKSbiIayY0hYWFPPvss2zfvp3XX3+dP/74g5YtW6LRaPD393dWjEII4fG0pnx0JgP5Lr7tQTG1vx6Vny8qmXISLuKaCc17771Hbm4ubdq0sR97+eWXyc7OZt68eQ4PTgghPMHatWtZu3btNc9xpyrBxbSRYTJCI1zGNROazZs3M2fOHMLCLn2DRUZGMnv2bDZu3Ojw4IQQwhPo9Xr0+ms/il1cJdhdppygqFqwJDTCVVwzofHy8sLHx+eK4/7+/uh0OocFJYQQnuTDDz/kww8/vOY57jhCo4kMk8e2hcu4ZkKjVqvJzc294nhubi5ms9lhQQkhhCdZsmQJS5YsueY57jhCo40MQ50hCY1wDddMaOLi4pg+fToGw6VKkAaDgenTp3PHHXc4PDghhKgqfA1pWNRajD5BSodSZpqIUFQGI9a8fKVDEeLaCc39999PQEAAt9xyC0OHDuXuu+/mlltuITAwkP/7v/9zVoxCCOHxfPPSKNCHgkqldChlpvm3Fo3sui1cwTW3PlCr1bz88stMmDCBQ4cOoVaradGiBZGRkc6KTwghqgRfg/vUoCmm/bcWjTk5Da+61RWORlR1ZdrLqUaNGtSoUcPRsQghRJWlz0slO7im0mGUy6URmjSFIxGijAmNEEKIitu8efN1z/HNS+VC9TbXPc+VFG9/YEmWhEYor0xbHwghhHAcjdmItzHH7aac1KFB2DRqzJLQCBcgCY0QQjjYW2+9xVtvvXXV94tr0Bj8wp0VUqVQqdXYQgJkUbBwCZLQCCGEg61Zs4Y1a9Zc9X3fvFQA8t0soQGwhgTKCI1wCQ5NaBYtWkS/fv2Ii4tj0qRJpKWlYbFYeOWVV+jduze9evXi22+/tZ9/+vRp7r33Xvr27cvdd9/NiRMnHBmeEEK4BL0bJzS20EBZQyNcgsMWBR88eJDPPvuMlStXEhAQwBtvvMHcuXOJjY3lzJkzrFmzhry8PIYNG0azZs1o2bIlkydP5v7776d///5s2bKFxx57jDVr1qByo7oMQghRXr6GooTG3aacAKyhgVhOxisdhhCOG6Fp3rw5P//8MwEBARiNRpKTkwkODmbjxo0MHjwYrVZLUFAQ/fr1Y9WqVSQnJ3Py5En69esHQNeuXcnPz+fQoUOOClEIIVyCPi8Vi0ZHoXeA0qGUmy00EEtqBjbZDkcozKGPbXt5ebFx40amTZuGTqfjscceY/369URHR9vPiYqKIj4+nqSkJCIiIlCrL+VYkZGRXLhwgWbNmpW5z4MHD95w3Lt27brhNoQy5N65L0++dyaTCbjsa1TXJjEx0f5+29Sz5HgHk5iUpER4ZVYP3RX3SRcSADYbe375DVuY+2zbIIp40vedw+vQ9OzZk549e7JkyRIefPBBtNoru1Sr1Vit1lKv12g05eqvefPmeHt7VyhWKLq57dq1q/D1Qjly79yXp9+7bdu2lXi9YU8qMTEx9tdhVgOFQZEljrmm1Cvu074/i36JbBoRg0/rxkoEJSrI3b7vjEbjNQctHDbldObMGf7++2/76yFDhpCYmEhERAQpKSn248nJyURFRRETE0Nqaio2m+2K94QQwpP55qVi0Lvf+hkAW1ggAJYLqQpHIqo6hyU0KSkpPPnkk6SnF9UnWL16NQ0bNuSOO+7ghx9+wGw2k52dzY8//kjPnj2JioqiVq1arF27FoCtW7eiVqtp1KiRo0IUQginePnll3n55Zev+r4+L8Utn3ACsP47zWROSrnOmUI4lsOmnNq3b8/EiRO577770Gg0RERE8MEHHxAdHU1CQgIDBw7EZDIxbNgwbr75ZgDefvttnn/+eebPn49Op2Pu3Lkl1tQIIYQ72rRpEwDPP//8Fe9pCw14mfLd8gknAFtwAKjVWC7Io9tCWQ5dQ3Pvvfdy7733XnF82rRppZ5fp04dvvrqK0eGJIQQLsWda9AAoFGjiQjFLFNOQmEy/CGEEAoqrhLsriM0ANroajLlJBQnCY0QQijI7UdoAE1UGJZkGaERypKERgghHCwsLIywsNJ30vaIEZqocMxJktAIZTm8Do0QQlR1P/zww1Xf0xtSMXn5YtbpnRhR5dJGVcOakY21wIjap+J1wIS4ETJCI4QQCvLNS3Pr0RkomnICZJNKoShJaIQQwsGmTp3K1KlTS33PNy/VrdfPQNGiYECmnYSiZMpJCCEc7M8//7zqe/q8VFIjy75fnSvSRBUlZFItWChJRmiEEEIpNlvRtgd+pS8YdhfafxMaqUUjlCQJjRBCKERnzEZrKXT7KSd1cAAqHx3mC1KLRihHEhohhFCIX25RApDnH6FwJDdGpVKhiQqX7Q+EomQNjRBCOFiNGjVKPa7PvQiAwc0TGih6dFuqBQslSUIjhBAOtmjRolKP6z1khAZAGxWGcf9RpcMQVZhMOQkhhEL0eSlYVRoK9KFKh3LDNNHVMF9Iw2azKR2KqKIkoRFCCAd74okneOKJJ644rs+9SL5fGDa1xvlBVTJtVDg2Qz7WnDylQxFVlEw5CSGEg+3du7fU4365KRj8qjk3GAe5vBaNJtBf4WhEVSQjNEIIoRB9XgoGf89IaKQWjVCaJDRCCKEEmw19zkWPS2gssv2BUIgkNEIIoQCvwly8zPnk+bn/E05QtCgYwJx4UeFIRFUla2iEEMLBGjVqdMWx4qJ6njJCo/b1Rh0aJLVohGIkoRFCCAf75JNPrjim97CEBkAbE4H5vIzQCGXIlJMQQihAn1f0gz/PP1LhSCqPNqaaJDRCMZLQCCGEg40fP57x48eXOKbPTcGGiny9e++0fTlt9QiZchKKkSknIYRwsKNHr9wSQJ97kXx9KDaN5/wzrI2uhjU9C6uhALXeR+lwRBUjIzRCCKGAoho0nvGEUzFt9aKvR0ZphBIkoRFCCAX45XpOUb1i2ph/Exp5dFsoQBIaIYRQgN6Dtj0oZk9oZGGwUIDnTN4KIYSLat26dYnXWkM+usJc8jxshKa4uJ4lUaachPNJQiOEEA727rvvlnjtm5oG4HFraNS+3qjDgmTKSShCppyEEMLJLiU0njVCA8XF9ZKVDkNUQZLQCCGEg40aNYpRo0bZX+svFlcJ9qwRGvg3oZGnnIQCZMpJCOEQ1lwDxn3xFPz9D5b0LNQ+3miqheDX91b74tGq4ty5cyVe6y+mYFWpPWZjystpYyIo2LFf6TBEFSQJjRCi0thsNgr+3Ef2V6vIW70Fm7EQAJWvN7Z8IwCpz83F99a2hE6fgE+bJkqGqxi/5BTy/cI9qqheMW1MNayZOVjz8lH7+SodjqhCPO+7SQihiPw/9pL2yscYdx5EHehPwL390N/RGZ+2TdCEBmGz2TCdPEfuso1kf7mK8/0eJmzGwwSNv1vp0J1OfzGFvADP2cPpcpcX19M1qKVwNKIqkYRGCHFDjAePk/7Kxxg2bUcTFU74m08RMLT3FaXvVSoVuvo1CX16LEHjhnDxsddIm/4ehQeOwpg7FYpeGfrkFFICWiodhkNcXotGEhrhTJLQCCEqxHTqPOlvfEruDxtQB/kT+sJEgh4cUqY9fDQhgUR9OYuM2Z+R8dZCfLQ2aN/eCVEro1OnTvY/2ywW9Cmp5MZ46AiNVAsWCpGERghRLuakFDLe+Yrsr1ah8tIS/Pgogh+5F01wQLnaUalUhDzzAObEFPj6R3I6tydgaG8HRa2s1157zf5nS3IaaouFvIAoBSNyHE10OCAJjXA+SWiEEGViSkgi872vyf52LVitBI6KI+SpMWijwivcpkqlotqbT5H+z1Eu/mc23m2bevw0hensBQCPTWjUPt6ow4OlWrBwOqlDI4S4psITZ7n42GskdBhB9jc/EjiiL7W2f0O1NyffUDJTTKXzwvDsaNQ+3qQ+MwebzVYJUbuWIUOGMGTIEADM54qKzuV66KJgAG10NUznpLiecC4ZoRFClKrwyCky3v2K3OWbUOm0BI29i+BHRjikhowtNJDQ5yeQ+vQccr//2eOmntLS0ux/Nv87QmPw4ITGq2YUhSfOKh2GqGIkoRFClGA8cIyMt78gb80WVHpfgiYNJXjScLSRYQ7tN/C+AeQsXkfqC++j79UZTUigQ/tTivlcMsagQMxenlujRVszCsPmndhsNlQqldLhiCpCEhohBO/tS0edcJYWny2ixu/bMen1HLv3Ho7dFUdhUCAk2iAx1XEBqGuzYV86QQ8+SK+Hn+KnFz7j4AOjrnuZXq3msVahjovLAUxnL5AX4Xl7OF1OWzMam6EAa1ommvAQpcMRVYQkNEJUcdZcA43mfUL9VeuwaHXsu+kBDre6G5N3AGx2TgyJiYnExMRQQDhn6t9Ogx/Wst/vAQp9rjNKM9CBSZaDmM9dwBAZrXQYDuVVs2g6zXQuWRIa4TQOXRS8cuVKBgwYwMCBAxk+fDgHDhwA4KOPPqJ379706tWLefPm2RcBpqenM27cOPr27UtcXBy7d+92ZHhCVHmG3/7m7G3302DVWo4168+KUYvZf/PYomRGIQfa34eXKZ/G+75XLIbK1qNHD3r06IHNZsN8LhlDFRihATAnJCkciahKHDZCc/LkSd58802WLVtGREQEW7Zs4dFHH2XmzJmsW7eOZcuWodFoePDBB6lfvz59+/Zl5syZtG/fnokTJ3L48GHGjx/P+vXr8fX13LlmIZRgs9nImLOQjDc+w6t+TX59exbnj3dROiwAMsPqc6ZeV5rsX8rh1sMwefsrHdINe/755wGwpGdhMxR4fkJTq+iR9OIF0EI4g8NGaHQ6Ha+88goREUVPRDRv3pzU1FTWrVtHXFwcer0eb29vBg8ezKpVqzCbzWzevJmhQ4cC0KRJE+rUqcPWrVsdFaIQVZLVUEDyQy+S8cZn+A/tTY1fPyetWWOlwyrhQPv70BXm0vjAD0qHUqmKf8B7+hoaTVAA6gA/e80dIZzBYSM0NWrUoEaNGkDRb4OvvfYa3bt35+LFi3Tpcuk3waioKJKTk8nIyMBqtRIaemmBX2RkJBculO8b4uDBgzcc+65du264DaEMuXfXYSzEb8YCtPuOU/DgADLvvp1zhw6CujaJiYmKhnZ5/4n40zSyNfX3/cCGqNuxqkv/p6oeOre454899hgA84eNww8wRFYj8YCyn3dFXO3zLu1YQHgQaQfjSXCD+1OVucP3T1k5fFGwwWBgypQpXLhwgf/+97888cQTV5yjVquxWq2lXq/RaMrVX/PmzfH29q5IqEDRzW3Xrl2FrxfKkXt3bTZjIRfun4Zh33Ei3n+uRK2XDXtSiYmJUSy24kXBlzvTbhj11k7lJuMpztbvepUrU93innt5eQFQS+dHGkUJjZKfd8Vd+Xlf7fsuKbYe5jOJNHaD+1NVudu/mUaj8ZqDFg5dFJyYmMjw4cPRaDR8+eWXBAYGEh0dTUrKpZLYycnJREVFERZWVOMiKyurxHuRkZ5bfEoIZ7HZbCQ//AqGTdup9tZktyhcd752J/L8I4g9uELpUCqN+dwFVHpfCgOUW3TtLF41IjGdveCRlZ+Fa3JYQpOZmcmoUaO44447eOedd/DxKdqBt0ePHqxatQqDwUBhYSHLli2jZ8+eaLVaunXrxuLFiwE4cuQIJ06coEOHDo4KUYgqI3PeN+St+pXQFyYSeN8ApcMpE5taw7FmA4g+9zcBmZ5RddaUkFS0YLYKFJvT1orClmvAmpmjdCiiinDYlNO3335LUlISGzZsYMOGDfbjCxcu5I477uCee+7BZDLRo0cPBg0aBMCMGTOYPn06cXFxqFQqZs+eTUAV+E1GCEcy/PY36a9+gv+g7gQ/cq/S4ZTLsSZxtNz5OY0OrmRXl0eUDueGmU8n4lWnutJhOIX90e2zFzy26rNwLQ5LaCZNmsSkSZNKfW/ixIlMnDjxiuPh4eF89NFHjgpJiCrHnJxG8oSZeDWsRbV3nnW7MvQFfmEk1L2N+kfWsqfTeKwandIhVUhcXBw2mw3T++vx7dpe6XCcwqtm0aPbprMX8G7ZSOFoRFUgu20L4cFSn30HW46BqM9eRu2vVzqcCjnRpC/exhyqn9mudCgVNnnyZP4zeiw2Q0EVGqEprkUjxfWEc0hCI4SHyl31K3k/biHk2QfQNaqjdDgVllSzPfm+IdQ9ul7pUG6I6XTRY9pVJaFRhwSi8vPFfDZZ6VBEFSF7OQnhgSzpWaROeQfvVrEETxqmdDg3xKbWcrphTxodXIGXMUfRbRkqqlu3blguprOQILR1YiBb6YgcT6VSoa0ZhUlGaISTyAiNEB4o/dVPsGRkU23uFFRa9/+95WTsHWisJmqf2Kx0KBVmKzCCWm1fW1IVeNWMwpwg1YKFc0hCI4SHMR46QfaiNQQ9MBjvZg2UDqdSpFeLJSu4FnXj3XfayVpQiLZGJCqdl9KhOI22ZjTmc5LQCOeQhEYID2Kz2Uib8QHqQD9CJo9ROpzKo1JxKvYOohL34pftnj8gbQVGvOq4Y3XgivOqFYU1KxeL1KIRTiAJjRAexLBxO/mbdxIyeazH1f441bAXALXcdNqpKKGpGguCi3nVLfp6TafPKxyJqAokoRHCQ9isVtJemo9XvRoEjR2kdDiVLjcohrRqjdxyHc09AwfRRx2CtnbVGqHR/pvAmU6dUzgSURVIQiOEh8hb+SumI6cImfKgx67TSKjfjWrJ/6DPca9Hgcf17Mco3+iqN+X0bwJnPuV+O4sL9yMJjRAewGaxkP7W53jF1sF/wO1Kh+MwZ/7ddbvWyS0KR1I+WfEnyLdZqtyUk9rPF01UuIzQCKeQhEYID5C78ldMR88QOnksKo1G6XAcJie4Fulh9d1u2mnw1P/wYNY/9jUlVYlX3eqYTskaGuF47l+gQggX8t6+dAxWq3M7tVi485X/YqtTi+9rNoc9qc7t38kS6nej9V+f4pubgg332JvKVlCIykuLOsBP6VCczqtuDQwb/1Q6DFEFSEIjRCUyWK0UrAx3ap+1jv9K4Nnz/HbHTApWR1SoDZ+B7pMEnfk3oal1cgtn6KZ0OGViKzCi8vFWOgxFeNWtjuViOtZcg2L7iSnyi0Yl0KvVPNYqVOkw3IYkNEK4M5uNZnu+JTuoBgn/ri/xdNmhdcgMqU3Nk1vdJqGxFhRWydEZKBqhgaK9rLybK1Po0dm/aKgsZuoe20BA1nm8Cg3k+UdwvGm/8m/b4Ua/aLgCSWiEcGOR5/cQfvEw27tOxqb23LUz/+tc3S403fMdXjm5gHNHxMrLWmDEZixEXS1E6VAUUfxkl+nUOcUSGmeKOf0n7be9T1BmAjZUmLx80ZkMtPx7IfEtBrP/prFYNZ75FKLSJKERwo012/MN+b4hnGzcW+lQnOps3Vtpvvtronf8DbfVUTqcazKdOs8Q7wgC+w1QOhRFaKtQcb3mf39Jmx0LyA6qwa99X+Ncnc6gUhOScpTmu7+mxa6vCE47yW+9X8Kq0SkdrseRp5yEcFMhqcepnrCDIy3vxqKtWuszUiObYNCHEvPnX0qHcl2mo2cY4hPJ2PHjlQ5FEZpAf9ThwR7/pFOjA8tos2MBJxv1YvWILzlXtwuoin7EZlRrxNY7Z7K961PUPL2NbmunojEbFY7Y80hCI4SbarJvMSatL0eb36V0KM6nUnOuThei/t6DzViodDTXVHj8DOk2M1lBvkqHohivujU8OqGpfWwTHX57h7N1u/BHj+euOqV0rPkg/rh9CjEJO7l5yxwnR+n5JKERwg35GNKpc3QTJ5r0odCnnAsNPcTZul3wyi/AsHW30qFck+lYAo8aTzD0vlFKh6IYr7rVMXtocT3f3BQ6bn6Li1HN+e2OF7Gpr72S40TTfhxsN5oGR36i5gn3KhDp6iShEcINNTq4Eo3VRHyLIUqHopgLNdpi9vHBsO53pUO5psKjp1H7Vq0pwf/lVbc65vMXseZ72DSLzUbHzW+itprY1nM61jJO/e67aSxp1WLpuPlNfPLSHBxk1SEJjRBuRm0ppNHBFZyr3ZHskFpKh6MYq9abC+3bkLfud2wuWmPEZrViOnEWla+P0qEoqvjRbXOCZ+3pVPfoz9Q48yd7Ok4gN6jsVaBtGi2/95yO1pRPxy1vOTDCqkUSGiHcTJ1jv+Cbn86RlvcoHYrizne+GUtyGsa9R5QOpVTm8xexGQpQ66t6QvPvk04nPWfaycuYS/vf3+didAuOtCz/SGl2aB323zSWmqd+J/L8HgdEWPVIQiOEO7HZaLz/ezJDapNU8yalo1Fc0s3tQKMh7yfXnHYyHTsDgKqqTznVrwlA4fEEhSOpPE33fodPQRZ/3fqE/Wmm8jrS6h7y/CNo+8eHYHPNUUZ3IgmNEG6kWtIBwlKOFo3OqNxjHyNHMgUG4NOxJXkuuo6m8GhRQjNp0iQmTZqkcDTK0QQFoIkMw/Tv5+HufAzpNNm7hNMNupNRrVGF27FovdnbYRzhF49Q+/ivlRhh1SQJjRBupMn+JRi9AzgZe6fSobgMv95dMB055ZKPBZuOJ6AOCWT4uAcYNmyY0uEoSteoNoVHTysdRqVovusrNJZC9nZ48IbbOtXoDtLD6tNm+8eoLaZKiK7qkoRGCDfhl32Bmie3cqzZACxeVXtNxuX8+nQBIG/dVoUjuVLhsTPoGtbm3LlznD17VulwFOXVqA6FR89gs9mUDuWG6HOSaXRwJSea9CEn+MYX5dvUGvZ2HE9AdhJ1jm2shAirLklohHATsQd+AFTEV8VCetfgVTsGXdN6LrmOxnTsDF4NazF69GhGjx6tdDiK0jWqgy3XgCUpRelQbkjTfYtRYWV/+zGV1ub52p3ICKtHsz3fylqaGyAJjRBuQFtooMHhNSTU74ohIFLpcFyOX+9bKdhxAEtaptKh2FkysrGkZKBrVEfpUFyCLrYOcGldkTvSFeTQ4J81nG7Qo3K/D1Uq/mlzL8Hpp6h+5s/Ka7eKkYRGCDdQL34d3sZcDreSR7VLo+/TBaxW8ja4zg+D4iecvBrUVDgS1+DVsDYAhfGnlQ3kBjQ8tAovcz6H2oyo9LZPN+hBrn8kzXZ/U+ltVxWS0Ajh6mxWmuz7npTIpqRGNVc6Gpfk3SoWTXQ1DD+5zjoa46ETAOiaNlA4EtegqRaCOiTQnui5G7WlkMb7viex5k1khFf+PbVptBxuPZzIpP1USzpQ6e1XBZLQCOHiapz+k8CscxxuNVTpUFyWSqXCr/ctGDbvdJny+oUHj6MODkBbPULpUFyCSqVC17C2247Q1Dm6Eb0hjUOthzusj+NN+2H09qfx/qUO68OTSUIjhItrsm8xef4RJNTvqnQoLs2vdxdshgLyf/tb6VAAMB48hq55Q1QqFU899RRPPfWU0iEpziu2DoXHTisdRvnZbDTev5TM0LoOLWhp9vLlROO+1Dq5Bd+8VIf146kkoRHChYWkHCPq/B6OtBhy3V18qzrfW9qg8teT5wLTTjaLhcJDJ/BuXjQ10b9/f/r3769wVMrTNaqDNS0LS2qG0qGUS3jyIcJSjxHf4i6HF7Q82nwQaquFBodWO7QfTyQJjRAurMm+JZi0vhxrJj8Mr0flrcOvZ0cM6//AZrEoGovp5Dls+Ua8mzcEID4+nvj4eEVjcgW6Rv8uDHazJ50aHVyBycuXk40cX9AyJ7gm52vdTKN/VqEymx3enyeRhEYIF+Wbl0qdYxs50aQPJu8ApcNxC/o+t2JJycC465CicRgPHgNA16xohGbChAlMmDBByZBcwqVHt08rGkd5eOdnUuf4L5yM7Y1Zp3dKn0ebD0afl0rMH385pT9PIQmNEC6q0cEVqK0W2VW7HPQ9OoBWo/jeToUHjoOX1j4iIYpoYiJQ+fm61Z5O9Q+vRWMp5GjzQU7r83ztjuQGRNNg9U9O69MTSEIjhAvSmI00OriCc3VvISe4htLhuA1NUAC+t7RRvGqw8eAxdLF1Uem8FI3D1ahUKnSN69ofaXd5NiuN/llJcnQrMsPqOa9btYZjTeOI2HcQ08lzTuvX3UlCI4QLqhv/Mz4FWRySR7XLza93F0zHEyg8nqBYDIUHj9sXBIuSvFs0pPDAMbfY0ynq/B4CshM51myA0/s+0bgPNrWa7G9+dHrf7koSGiFcjMpqoene70ir1oiLMa2VDsft6Hv/u1mlQk87mZPTsKSko/t3QbAoybtFI6zZuZjPJCkdynU1OLQGo7e/IiUT8v2rkXRzO3K+XYvNJIuDy0ISGiFcTM2TvxGUeZaDbUc5/BFRT+RVIxJdi4YYFJp2Kjx4HKDECM306dOZPn26IvG4Gl2LokTPeOCowpFcm64gi1ontnCq0R1YtN6KxHCyT08sF9MxbHSdLT1cmSQ0QrgSm40Wf39JVnAtzta7Telo3JZfn1sp+PsfzBfTnd63/QmnyxKanj170rNnT6fH4op0TeqBRoPxwDGlQ7mmevEb0FhNHG+qXMmECze3QxMZRvaiNYrF4E4cntDYbDamTJnCp59+CoDFYuGVV16hd+/e9OrVi2+//dZ+7unTp7n33nvp27cvd999NydOuMnCMSEqScyZ7YSmHedgu1HY1Bqlw3Fbfr27gM2GYf0fTu/buOcw2joxaIIuPWq/d+9e9u7d6/RYXJHaxxtdbG0KXXmExmajwaHVpEY0dsi+TWUOQ6MhYERfDBu3Y05KUSwOd+HQhObEiRPcf//9/PTTpUfPvvvuO86cOcOaNWtYunQpX3zxBfv37wdg8uTJjBgxgrVr1/Loo4/y2GOPucXCMSEqhc1Gi11fkusfyamGvZSOxq3pmjdAWzPK6etobDYbBX8dxOfmFiWOP/HEEzzxxBNOjcWV6Zo3wrjfdROasIuHCUk/yfEmcUqHQuC9/cBqJeebtUqH4vIcmtB8/fXXDB48mD59+tiPbdy4kcGDB6PVagkKCqJfv36sWrWK5ORkTp48Sb9+/QDo2rUr+fn5HDqkbIEsIZwl+uxfRFw4yD/tRmLTyDYHN0KlUuHX59aizSpz8pzWr/l0IpaUdHxukl3Rr8W7ZSMsF9MxJ6cpHUqpGh5ag1nrw+lGyk8TetWtju+tbcn+5kdsVqvS4bg0hyY0L7zwAoMGDSpxLCkpiejoaPvrqKgoLly4QFJSEhEREajVl0KKjIzkwoULjgxRCNdgs9Fm+yfkBkS7xG+FnsB/4O1QaHLqKE3BXwcArhihESV52xcGu946Gm2hgTrHNnK6we2YdH5KhwNAwKj+mBOSyN+6W+lQXJrTfw0sbQpJrVZjvUrmqdGUbx3BwYMHKxTX5Xbt2nXDbQhlKH7v1LVJTEws92Wx57cTlnKUNe0e5lyy8+fK66GrUNyVqSL910N39XuushJYLZjzX64gr361G4yubHx//AWdnw8H89JhV6b9eE5ODnDZ388K/j1R2tU+73J/35kLCAZOrfsVY4gTig+W4/NucfpXvEz5bI/oqPg9sn/e0f4EBug5894XGPwr98lHxf/NrEROT2iio6NJSbn0D3ZycjJRUVHExMSQmpqKzWZD9e+jqsXvlUfz5s3x9q74I3a7du2iXbt2Fb5eKMcV7t2GPanExMSU6xqV1cztm5eRGVKbzJuHEqPIYuDyx12ZEhMTK9h/6jXveeo9vclasJTW9RuhCXb8flgJp+ai7dCKBjfdVOJ4QEBR38WxVuTviWu48vOu6PddQt0a+KXlEeWE79nyfN7t//ydrJDa2JrfToziZRMufd6pI/qR9flyGtWpjyYsuFJad4V/M8vDaDRec9DC6Y9t9+jRgx9++AGz2Ux2djY//vgjPXv2JCoqilq1arF2bdHCp61bt6JWq2nUqJGzQxTCqRocXktQxhn2dRgnTzZVMv9B3cFkdsq0kyUzB9ORU/iWMt00a9YsZs2a5fAY3ImuRUP7I+6uIijtFBEXDnKsSZzL1YAKGNkPTGZyvv9Z6VBcltNHaEaMGEFCQgIDBw7EZDIxbNgwbr75ZgDefvttnn/+eebPn49Op2Pu3Lkl1tQI4Wl0BTm03v4JydGtSKjn/Gqkns67dWO0taPJXfELgSP6OrSvgp1Fvzn6dLgyoencubND+3ZH3q1jyVv1K5bUDDThIUqHA0DDw6uxqLWcir1T6VCu4N20Pt7tmpL91WqCJgy1z2SIS5yS0Lz++uuXOtRqmTZtWqnn1alTh6+++soZIQnhElru/AydMYedtz7ucr8RegKVSoX/gO5kzv8OS1pmpQ3Vl6bgrwOg0eDdpskV7/3xR1E9HElsLvHt0BIo+tz8+ipfRFJjNlLvyDrO1ruNAr1rJFj/K3BUf1L+8wbGnVeWBhBSKVgIxQSnnST2wHKONRtARjXZ98dR/If0BLOF3OWbHNpPwc6DeDdvgNrP94r3nnvuOZ577jmH9u9uvFvFovLWkb/jgNKhAFD7+K94G3M42myg0qFclf+g7qj8fKVy8FVIQiOEAlRWCx02v4lJp2dvh3FKh+PRvJs1QNeiITnf/XT9kyvImpeP8e9/8OnY0mF9eBqVtw7vNk0o2LFf6VAAaPjPSrKCa5JcvY3SoVyV2l+P/109yF35i1PrK7kLSWiEUECTfUuIuHCQnbc+TqFPkNLheLyA4X0x7ovHeMgx26nk/74bm7EQfc9ODmnfU/nc3BzjvnishgJF4whOO1G0GLjZAJef+g0cFYfNUEDOso1Kh+JyJKERwskC00/Tesd/Sah7K6ca3aF0OFVCwJCe4KUlZ/E6h7Rv2PgnKr0vvp1aOaR9T+XToSWYLRj3HFY0job/rMKi0XGicZ/rn6ww77ZN0TWtR45MO11BEhohnEhtKeSWTa9i8vJlR7fJLv/boKfQhAXjd0dncr9fj81krtS2bTYbhg1/ou/WHpW3rlLb9nTFC1sLtis37aQ15VMv/mfO1O/mFqOlKpWKgJH9Me494pKVlpUkCY0QTnTT1vcIv3iE7bc/Q4E+VOlwqpSA4X2wpKRj+GVHpbZbePgk5vMXrznd9O677/Luu+9War+eQBMcgK5JPfIVXEdT59gmdIV5HG3uuouB/1fAPXeg8tbJ4uD/IQmNEE7S4NAaGv2zkoNtR3K2nvKPqVY1+h4d0USEkv35ikpt17Dhz6L2e109oWndujWtW7eu1H49hU+HFhTsPIjNYlGk/4b/rCQztC4pUe7zGLQmJBC/uK7kLl2PNd+odDguQxIaIZwgInEvN295m8SaN7G3w0NKh1Mlqby0BI4dhGHTdgqPJ1Rau4YNf6Jr0RBtVPhVz9m4cSMbN8oiztL4dGiJLddA4T+OWbB9LaEX4wm/eKToUW03m/4NHBWHNTuXvDWblQ7FZUhCI4SDhV48wu1rniU3KIatd8yQ7Q0UFHjfQNB5kfXfHyqlPUtGNgU7D+LX69oF81555RVeeeWVSunT0/jeUvSYtGHzTqf33eiflZi13pyMdb/F+T6dW6OtU53sr1YrHYrLkIRGCAcKTjtJj9WTKfQJYsOAd9xi0aEn00aE4j+oBznf/YQlO/eG28td+QtYrfj1vbUSoquatNHV0DVrYJ+6cxYvYy51jm7kdMOemLwdv3FpZVOp1QSOiqPgz30UHjmldDguQRIaIRwk8vwe7lz2f1jVWjYMfId8/2pKhySA4PF3Y8vLJ+ebH2+4reyvVqNr1gBdS9lE90boe3WiYOdBLJk5TuuzwaE1eJnziW9+l9P6rGyBo+JQeevI+rRyRhzdnSQ0QjhA3fif6bHqKQx+Yawb8hG5QdWVDkn8y7tVLD4dWpL10RJsxsIKt2PcF0/h/qMEju4vGwXeIL9encBiwfBr5T6BdjUqq5km+7/nQkxr0iNindKnI2jCgvG/qwc5S37GkuW8ZNBVSUIjRCXS5BfQ6ZfX6bLxFVKimvHz4PnkBUYpHZb4HyFPj8V8/iJZC1dWuI3sr9eg8tHhP6RXJUZWNXm3a4o6NMhp0061T2zGL/cih1sPd0p/jhQ0bkhR5eBKGHF0d07ZbVuIqiB/2x56PfI6/ueSONDuPvbdNBabRr7FXJG+a3t8b21LxrtfEjiyH2p/fbmut+blk7t0A34DbkcTfP31Fx9//HFFQ60SVBoN+h4dMGzagc1iQaVx4MJ5m40mexeTFVyTc3Xcf6sK71ax+NzcgqzPlhM0/h7HfnYuTkZohLhB5sSLJE+YSeKgx1CbzGwY+C57Oz4kyYyLC502HmtqJpkfLyn3tbkrijYHDBzVv0znx8bGEhvrvlMbzqDv1QlrehbGXYcc2k9E0j7CLx7hcKuhoPKMH4FB44ZgPp3o9IXVrsYz7qYQCjBfSCX1ubmcuWk4eT/+RsjkMaz773sk12irdGiiDHzaNcOv761kffAd5gupZb7Oaigg483P0LVoWObdtVevXs3q1fJ47bXob+8AGg15P29zaD8t/v6SAt9gTsb2dmg/zuQX1xVtzSgy532jdCiKkoRGiHIy7osn+f9e5Uy7oWR9tpyAe+6g5h+LCH32Qaze3kqHJ8oh9IVJ2MxmLj46C5vVWqZrMj/8FvP5i4S/8liZFwPPmTOHOXPm3EioHk8THIDvrW3JXbaxzPeivKol7Sfm7E7+aXMvFi8fh/ShBJWXlqCJwyj464Ci20goTRIaIcrAZjaTu2YL5/s/wrme48hbs4XA0f2p9cciIt6dgletaKVDFBWgq1+TsJceIX/zTrI/XXbd883nk8l872v8BnbHt3NrxwdYxQTc2xfzuWTyt+52SPut/vqcfN8QjjYf5JD2lRQ4sh/q0CAy3/9W6VAUI5P8QlxD4Ymz5HzzIzmL12FJTkNbK5qwl/6PgHv7oQlyv2Jc4kqB9w/EsP4P0l6aj0+HlnhfpaaMzWIh9bm5YLMRNmOSk6OsGvz63Io6yJ+cb35E37V9pbZdLXE/0ef+5u/O/4fZy7dS23YFaj9fgh4cTMabn1MYfwpdbF2lQ3I6GaER4n9Y8/LJ/u4nzvd/hLMd7yXzg+/wbt2YqK9eo9Zf3xI8abgkMx5EpVJR7d0pqEODSbzrcfL/2HvFOTaLhYuPvUbe2q2ETn0Ir5ryKL4jqH288R9yB3k//la5RfZsNlrvWOCxozPFgh4cjMrXm4z3vlY6FEXICI1wSe/tS8dQ3nl0dW027Cn74s4SbDZC4o9Rd90mam3eipchn5zq0Zx6YBRnet1OQVho0Xn7MyrWvnBp2ohQqv/4IUnDniJp6FOEzZiE/+CeqEODMB1PIP2Nz8hb+QuhU8YR/LD71y5xZYH39iX7s2XkLt9I0NjKqeJbfeufRCXuZUfXJz1q7cz/0oQFE/jAXWTNX0LI46PQNaqjdEhOJQmNcEkGq5WClVffvbg0iYmJxMTElOsafU4ydY7/Qr0j6whJP4lZ682Z+rdzvGk/Lka3KtqB9/eyt+czsIIJlVCcV41Iqq/+gAv3TyP1ubmkPv8+msgwLIkXAQh9fiIhj42sUNtfffVVZYbq0XQtG6Fr1oDsRWsIHDPohqswWw0FtPpkIelh9TnWdEAlRem6Qh65l+yFK0mf/TlR/52pdDhOJQmNqHJ8DBnUOrGZOsc2EplU9ERAakQTtnedzOmGPTB5+yscoVCKJjSImFXzKDx4nNzVmzEdT8D3iVH43dEZbfXICrdbs2bNygvSw6lUKoIevIuUJ9/EsOFP/O649k7m15P5wbf4XUzh90HPV4md7jXhIQRPGErG219gfGI03s0bKB2S00hCIzyeymIm/OIhohN2EnN2J2EXD6O2WckMqcPeDuM43aAHOcE1lA5TuAiVSoV3i4Z4t2hYaW0uXrwYgGHDhlVam54sYHhfMt77hvRZC9D37IhKXbHlnoXHE8h8bxFnu97CxeptKjlK1xX08DCyPv2B9NcWEP31G0qH4zSS0AiXY7PZwGar0LVqSyH+2UkEZp4lNOUYYSlHiEjch64wD6tKTVpEEw60u4+EBreTGVavkiMXonTz588HJKEpK5WXltApD3Jx4kvkrfwV/7t6lLsNW6GJ5AkzUel92TvxgXJNHbs7TVAAwY+OJP2VjzFs+bvSnxhzVZLQCKcxJ6VQ8NdBCg+doPB4AuYziVhSM7CkZWIzWcBqtScy9wAWtRcWrTcWja7o/1od5n//bNXosGi8UFvNaCyF6Apy8MpLR1+Yg4qiNmyoyA6pxekGPUiqdRMXqrej0EeeThLCHfjf1YPM9xaR/vp/8YvrisqrfD+u0l9bQOH+o0R9OevSov4qJGjCPWQvWk3qc+9Sc/PCcn9+7sjzv0KhGJvJTP5vu8j7eRuGTdsxJyQVvaFW41U7Bm2dGHSN66IOC0blrUOlVhUtwlWr+T0xF9shLzRmIxpzIRpL0f+1lkuvtSYDVrUWq0ZHVkht0oMaoK5Wi5ygGHKCapAZWhezrnybDgohXINKrSb0ufFcGDWF9Dc+JWz6hDJfm/fzNjLf/5bAMQPx63MrVPTpRzem9vEm/OVHuTB6KlmfLiN44lClQ3I4SWhEpSs8corsRavJ+WED1tRMVL7e+Ha9iaDx9+BzUzO8mzVA5a27ZhuH9qQ65SknIYTr8rvzFgJG9ydz7iJ82jbBr+9t173G8NvfJD/4At6tGxM28xEnROm69Hfegm/3DmTM/gz/u3qgjQxTOiSHkoTGw1WonktF2GxE7dxNox9WE7lnHxYvLUkdbuJMz65caNe65B5Hh7IdH48QwiNUe+0JCg8eJ/n/XqXGuprXrICbv20PF0ZPxateDaIXv4Va77k1Z8pCpVIRPusxznUdS8rkt4j6ctYNPwbvyiSh8XAVqedSXuEXDtL2j/lEJu0nz68auztO4HjTOIy+wZACrCt/m1LPRXiSpUuXKh2C21J564j6/GXO9XqIc3dOIPz1/xAwrHeJH8zWAiMZb35O5vvf4lW3OtHfv40mNEjBqF2Hrn4tQqc9RNoLH5CzeB2Bw/soHZLDSEIjKiwwI4HW2z+h9skt5PuGsr3rUxxvEodNI3+thLhceLhjf6nwdNrqkVRfv4CLD79MyqOzyPnuJ3xvaYNXrWgKdh2yr9ELuLcfYS8/giZQakldLmjCUPJ++p205+aiv7XtDdVUcmXyk0eUm29eKi13LqTBoTVYtDr23vwAh1sNkwW4QlzFwoULARgzZoyicbgzrxqRxCyfS+aH35GzeB0Zb34ONhsqvS8+HVtSbfZT6Ht0UDpMl6RSq4l47znOdhvDhYdepPryudddx+iOJKERZeZVmEfTPd/SdO9i1FYTR5sP5ED7MRToQ5QOTQiXJglN5VBpNIQ8OpKQR0dizTVgPpeMV/2aVeKR5BvlVSeGiPemkvzgC6RMeYdqbz+jdEiVTv4WiOtSW0w0/GclLf/+Ap/8TE436M7eDg9JdV0hhGLU/np0ja++QFhcyX/A7RifGE3mu1/h3aIRtKqldEiVqmL1pEXVYLNS+9gm+n8zmpu3ziUztB5r7/6ErXfOlGRGCAebOVPFzJkqMjJOXvHe339/xMyZKn75ZboCkV3d77//Tq1aV/6QjI2NRaVSlfhv7969pbZhs9mYNm0aERERhISEMHnyZCwWi/393bt3061bNwICAqhbty6vvfYaVmc8yekhQqeOQ39HZ1KnvovX5t1Kh1OpZIRGlCrq3C7a/jGfsJR4MsLqsSnuTRJrdSgqfCeEcAq12ov4+NV07Ph4ieNHjqwAXOt78cCBA9x9991otSV/rBiNRk6cOMG2bduoV+/SdiNXWyj9zjvv8MUXX7B06VKsVisjR44kPDycKVOmkJ6eTp8+fRg6dCiffPIJR48eZcyYMfj7+/Poo4869OvzFCq1mshPXiRpxNPYZi8it1FD/Pt3UzqsSiEjNKKE8AsH6bHqSXqtfAKf/Ay29ZjGj0M/I7F2R0lmhHCy2rVv4+jRVSWOGY3ZnD37B9HRrrPZ4scff0znzp2JjLzy6Zn4+HhUKhU33XQTUVFR9v/+N/Ep9u677/Liiy9y22230a1bN9544w0++OADANauXYuXlxdz586lUaNGxMXF8eSTT/L111879OvzNGo/X6K/mY0lthbJ418k++sflQ6pUsgIjQCgWtJ+Wu5cSMzZnRT4BLGr88McaTEYq9b7+hcLIa5p7dq1FbouNnYg69c/RUFBFj4+RXVVjh1bS+3at1JYmFfi3CNHVvDLL9PIyDhFeHhjevSYRYMGvQEwGnP4+ef/EB+/ioKCTEJC6tK9+yyaNh0CFE1vDRr0BX/88RZpaUeJiWnHoEFfEhpaH4B3361D69Zj6D2w9Mq7P/30E1988QXZ2dlMn15yGuzQoUPUq1cPLy+v6369iYmJnD17lttuu1QRuEuXLpw7d46zZ8/StWtXvvvuO9SX7b6tUqnIzMy8btuiJLW/ntxXJhA9bxkpT7xO4bHThD0/EZVGo3RoFSYjNFWZzUp0wg56rvwPvZf9H6Gpx9jVaRLLRy/hUJsRkswIUUn0ej16ffnLGlSr1oTg4DocP/6T/Vh8/EpiYweVOO/ChX0sXz6aW26ZwqRJB2jXbjyLF9/FhQt7Afj55/+QmnqY0aPX8/DD/1Cr1m2sXv0QZrPR3saWLTO58853GD/+b/Lz0/nll+fs7z300E46d5581ThXrFjB4MGDS33v0KFDaDQa+vTpQ1RUFF27dmXHjh2lnpuUVLTf2+VbmBSP+pw7d46aNWvSpUsX+3v5+fksWLCAnj17XjU2cQ1+vkR/O5vABweT9cF3JA56HNPJc0pHVWGS0FRBPoYMmu3+mkGLRtBz9WSC007y9y3/V5TItL1X6skIUck+/PBDPvzwwwpdGxs7kKNHVwNgsZg4fvxnGjceWOKcP/98i9atH6BVq9GEhtanffuJNG8+nB075gFQq1YX+vX7iKio1oSFNaRz58kUFGSQk5Nob6NDhyeoV68HERHNad9+EufP77S/5+dXDZ2uYsXqDh8+TEZGBg8//DBr166ladOmdO/endOnT19xrsFgAMD7sq1Siv9sNBpLnGuxWBg1ahR5eXlXjAqJslNptVR7/T9EvD+NwkMnONttDBnzvsaab7z+xS5GppyqCI3ZSEzCDuoc20TNk7+hsZq5UL0NezqO52y9W7FqPK/IkhCuYsmSJQA8/PDD5b62ceOBfPvtAKxWM6dO/UJERDP8/CJKnJOScpiLFw+wZ8+n9mNWq4nq1W8GoFWr+zhyZAW7dy8gNfUISUm7ALDZLj09VDy9BODtHYjVaip3rKX55ptvyM/PJzAwEChK7rZt28aXX37JCy+8UOJcH5+ivZeMRqN9iqo4kbl8hKuwsJB7772X9evXs3HjRqKioiol1qosYFhvfG9rR8rTc0h/6SOyPllKyJP3ETC0N2o/X6XDKxNJaDyUzWql8J8TNPxhK+E/xxN5fg9epnyM3oEcbX4XR5sNIDu0jtJhCiGuo2bNzqjVWhISfic+fiWNG991xTlWq5lOnSbTps3YEsc1mqLRjeXL7+Ps2W20bDma9u0nERAQzaefdvqfc0v+UmOz2Solfi8vrxLrZ1QqFY0bN+b8+fNXnFu9enUALly4QIMGDex/BoiOjgaKppnuuusu/vzzT9atW0eHDlIduLJoo6sRveh18rftIf3VT0h95m3SX/4Y/7vvwP+uHvjc1AzVVRZzuwKXi2zz5s3MmTOHwsJCYmNjmTVrFv7+yu7LYfj1L0ynzqPSakCrKapKqdWg0mpReWlR+fqg9vdF7adH5eeL2s8Xtb8ele76i+AqgzXfiOl4AoVHT1MYf5rCIycp2L4fa0Y2rYHsoBqcbHQHZ+t35UJMG9lrSQg3olKpadQojvj4VRw9upoxY3674pzw8FgyM08SGtrAfuzXX2eg14fRuvUYDhz4hgce2EbNmkVJzLFjRYuUKytpuZabb76ZIUOG8OyzzwJgtVrZv38/EyZMuOLcmJgYatWqxe+//25PaH7//XdiYmKoWbMmACNHjmTHjh1s3LiRm266yeHxV0W+t7Qh5scPKdhxgOyvVpHzzY9kf74cdUggvre1x+em5vi0bYJXg1poQgKVDtfOpX6ypaenM3XqVL799lvq1KnDm2++yVtvvcWLL76oaFypU9/FdOJsua+zarWYfX0w+fpi9vXB4uON2cen6D/fkv+3+Phg8dZhU6v//U+FTa1GZbGiKSxEU2hCbSpEW2DEOzMLn8ysov9nZOKTnoHq33+YrGo1uTHRpN3cnoutWpDSqjkZfzSu7I9ECJdgMcPre1x/Z/aE3KLpm4rGGhs7kOXLRxMSUo+QkCur43bs+B8++6wLMTE3Exvbn5MnN7J166uMGLEKrdYHnc6Pw4eXERAQTVraUdauLXpayWIp2zqJvLwUvLx88alA7HFxcbz55pu0aNGC+vXr8/bbb5Oens4DDzwAQG5uLvn5+VSrVg2ASZMmMXXqVGrVqoVGo2Hq1Kk8/nhRHZ7FixezfPlyFi1aRM2aNe2jNxqNxn69qBwqlQrfji3x7dgS6+v/wfDrX+T9vI2CbXvIW/mL/Tx1cABedWvgVbc62uqRaKoF49O+OT43NXd6zC6V0Pz++++0aNGCOnXqADBixAgGDhzIjBkzSmwVX5ri3zQKCwtvOI7/XXwWseETPvs7EdPmANRWM2qrFaxm1DYLaqsFjdmI1lyA1pSP1vTvn835aEwFeJnz0ZoL0JiMaM1GNKYCvLPy0adn4GUqQGMuQGsqQEXZflOyqL0o9AmkwDcYQ2BdMiKCMfiFkR1ck5ygGuQFRmNV/3tbreAdnoGvr/st7vK2mVGVM25/f7PiX2tF4nYFSsdd0XvnrTFjXO/6e4mFqYrWvKjWBwDg3SujTJ+3TleIr6+RZs1uY9kyM82bx9k/J7XailZb9Lk1bNiG4cMXsmnTq2zaNIWQkNrcc88ntGzZA7AxbNjnrF37LDt3vk9oaB169HiGDRteIS3tL2rXbliiLwAvLxMqFfbXc+e2p1270cTd/cgV/z7CpX8zTSZTidcAkydPxmg0MmHCBFJTU7n55pv56aef8PHxwWg08vrrr/PVV19x9OhRAB577DEuXLjAkCFD0Gg03HfffTz66KMYjUYWL14MwKhRo0r0X716dU6cOHHVz9HHjb8vS/u8K1OZ2tdp8bqzM8F3dgbAcjEd45FTmBMSMZ9NxnTuAnmnzmLesQ/MFrxqRBGz8r1Kj7X45/vVRhZVNmeMOZbRJ598wrlz53jppZcAMJvNNGvWjF27dl132iknJ8f+DSGEEEIIz9SoUSMCAgKuOO5SIzRX24/j8iJKV+Pn50ejRo3w8vK67miOEEIIIdyLzWbDZDLh5+dX6vsuldBER0ezb98+++vk5GSCgoLKVJBKrVaXmrEJIYQQwjMUP9pfGpcqrNelSxf27dtnL7j03Xff0aNHD2WDEkIIIYTLc6k1NABbtmxhzpw5mEwmatWqxRtvvEFwcLDSYQkhhBDChblcQiOEEEIIUV4uNeUkhBBCCFERktAIIYQQwu1JQiOEEEIItycJjRBCCCHcXpVPaGw2G1OmTOHTTz8FwGKx8Morr9C7d2969erFt99+q3CE4mr+994VFBQwdepU+vfvT79+/Zg6dSoFBQUKRylK87/37nKPPPKIvVq4cD2l3buvv/6au+66iz59+jB58uRK2YJGVL7Sft7NmDGDvn370rdvX9544w2nbFjqKFU6oTlx4gT3338/P/30k/3Yd999x5kzZ1izZg1Lly7liy++YP/+/QpGKUpT2r2bP38+FouFlStXsmrVKoxGIx9//LGCUYrSlHbvii1YsIC///5bgahEWZR279avX8+iRYv4/PPP+fHHHzEajSxcuFC5IEWpSrt3K1eu5NSpU6xevZqVK1fy119/sW7dOgWjvDEuVSnY2b7++msGDx5MTEyM/djGjRsZOnQoWq2WoKAg+vXrx6pVq2jZsqWCkYr/Vdq9u+mmm6hevbp9q4wmTZpw/PhxpUIUV1HavQPYvn07W7duZfjw4WRnZysUnbiW0u7dihUreOCBB+z1wmbOnGnfpFK4jtLuncViIT8/n8LCQqxWKyaTCW9vbwWjvDFVeoTmhRdeYNCgQSWOJSUlER0dbX8dFRVl36JeuI7S7l2XLl2oW7cuAOfPn+eLL76gd+/eCkQnrqW0e5ecnMyrr77KW2+9hUajUSYwcV2l3bvTp0+TlpbGgw8+SP/+/Zk3b55sQ+OCSrt3gwcPJjAwkNtuu40uXbpQu3ZtunfvrkyAlaBKJzSlKW3+sCybYwrXcfDgQUaOHMmoUaO4/fbblQ5HXIfJZOLJJ5/kueeeIyIiQulwRDmZzWa2bdvG3Llz+eGHH8jKyuKdd95ROixRBu+//z6hoaFs27aN3377jczMTD777DOlw6ow+Un9P6Kjo0lJSbG/Tk5OJioqSsGIRHn8+OOPPPDAAzz11FNMnDhR6XBEGRw8eJBz587x+uuvM3DgQL777jvWrl3LtGnTlA5NlEFERAS9evXC398fnU7HgAED2Lt3r9JhiTLYsGEDQ4YMQafTERAQwF133cWOHTuUDqvCJKH5Hz169OCHH37AbDaTnZ3Njz/+SM+ePZUOS5TBunXreOWVV/j000/p37+/0uGIMmrTpg1btmxh5cqVrFy5kuHDh9O3b19effVVpUMTZXDnnXeybt06CgoKsNlsbNy4kRYtWigdliiDpk2b2hcJm0wmfvnlF1q1aqVwVBVXpRcFl2bEiBEkJCQwcOBATCYTw4YN4+abb1Y6LFEGb7/9NjabjenTp9uPtW3blhkzZigYlRCe7d577yUrK4vBgwdjsVho1qwZU6ZMUTosUQZTp061lynRaDR06tSJhx56SOmwKkw2pxRCCCGE25MpJyGEEEK4PUlohBBCCOH2JKERQgghhNuThEYIIYQQbk8SGiGEEEK4PUlohBCK6N69OwcOHCj3dcuWLWPChAkOiEgI4c4koRFCCCGE25PCekIIRbVo0YLx48ezbds2Ll68yH333ceYMWMA+Pjjj1m+fDlarZbatWvz+uuvA5CSksL48eNJSkpCo9EwZ84c6tevT05ODq+++ipHjx7FZDLRqVMnnnnmGbRaLe+99x4bNmzAy8uLkJAQXnvtNdk7SggPIiM0QghFFRYWEhISwnfffcd7773HnDlzMBqNbNq0iWXLlrF48WLWrFlDjRo1WLRoEQBnz55l2rRprF69mvbt2/Ppp58CMGvWLJo1a8ayZctYsWIFGRkZfP755yQlJfHFF1/www8/sGzZMm655Rb279+v5JcthKhkMkIjhFBcjx49AGjWrBmFhYUYDAb+/PNPevfuTVBQEFBUph2K1tC0bNmS2rVrA9CkSRM2bNgAwObNmzlw4ABLly4FoKCgAIDIyEgaN27MXXfdxW233cZtt91Gp06dnPo1CiEcSxIaIYTivL29AVCpVADYbDY0Go39NUB2djbZ2dkAaLWX/ulSqVQU7+BitVqZO3cu9evXt1+jUqlQq9UsWrSIAwcO8OeffzJr1iw6dOhQYt8vIYR7kyknIYRL6ty5Mxs2bCA3NxeAefPmsXDhwmte06VLFxYuXIjNZqOwsJBJkyaxaNEijhw5QlxcHPXr12fChAmMGTOG+Ph4J3wVQghnkREaIYRL6tq1K8ePH2fEiBEANGjQgJdffpn169df9Zpp06bx6quv0r9/f0wmE507d2bcuHF4eXnRp08fhgwZgl6vx8fHR0ZnhPAwstu2EEIIIdyeTDkJIYQQwu1JQiOEEEIItycJjRBCCCHcniQ0QgghhHB7ktAIIYQQwu1JQiOEEEIItycJjRBCCCHcniQ0QgghhHB7/w/mCUgzNqR1eAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax=sns.histplot(data=df, x='Inches', kde=True, bins=10, color='blue', edgecolor='skyblue')\n",
    "\n",
    "plt.xlabel('Inches')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Inches', fontsize=14)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "\n",
    "mean_height = df['Inches'].mean()\n",
    "plt.axvline(mean_height, color='black', linestyle='dashed', linewidth=1.5)\n",
    "plt.text(mean_height + 0.2, 1, f'Mean: {mean_height:.2f}', color='black', fontsize=14)\n",
    "ax.lines[0].set_color('crimson')\n",
    "plt.legend(['Quantifying Screen Sizes'], fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4165,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "plt.figure(figsize=(8, 6))\n",
    "\n",
    "scatter = sns.scatterplot(x=df['Inches'], y=df['Price'], color='b', alpha=0.9)\n",
    "plt.title('Scatter Plot of Screen-Size vs. Price', fontsize=16)\n",
    "plt.xlabel('Inches', fontsize=14)\n",
    "plt.ylabel('Price', fontsize=14)\n",
    "scatter.grid(True)\n",
    "scatter.xaxis.set_major_locator(plt.MaxNLocator(integer=True))\n",
    "\n",
    "sns.regplot(x=df['Inches'], y=df['Price'], scatter=False, color='crimson')\n",
    "\n",
    "plt.legend(['Data', 'Regression Line'])\n",
    "plt.tight_layout()\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4166,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
       "Touchscreen 2560x1440                              7\n",
       "4K Ultra HD 3840x2160                              7\n",
       "IPS Panel 1366x768                                 7\n",
       "Touchscreen 2256x1504                              6\n",
       "IPS Panel Retina Display 2560x1600                 6\n",
       "IPS Panel Retina Display 2304x1440                 6\n",
       "IPS Panel Quad HD+ / Touchscreen 3200x1800         6\n",
       "IPS Panel Touchscreen 2560x1440                    5\n",
       "IPS Panel Touchscreen 1920x1200                    4\n",
       "IPS Panel 2560x1440                                4\n",
       "1440x900                                           4\n",
       "IPS Panel Retina Display 2880x1800                 4\n",
       "IPS Panel Quad HD+ 2560x1440                       3\n",
       "2560x1440                                          3\n",
       "Touchscreen 2400x1600                              3\n",
       "IPS Panel Touchscreen 1366x768                     3\n",
       "1920x1080                                          3\n",
       "Quad HD+ 3200x1800                                 3\n",
       "IPS Panel Quad HD+ 3200x1800                       2\n",
       "IPS Panel Full HD 2160x1440                        2\n",
       "IPS Panel Touchscreen / 4K Ultra HD 3840x2160      2\n",
       "Touchscreen / Quad HD+ 3200x1800                   1\n",
       "Touchscreen / Full HD 1920x1080                    1\n",
       "Touchscreen / 4K Ultra HD 3840x2160                1\n",
       "IPS Panel Touchscreen 2400x1600                    1\n",
       "IPS Panel Full HD 1366x768                         1\n",
       "IPS Panel Full HD 1920x1200                        1\n",
       "IPS Panel Retina Display 2736x1824                 1\n",
       "IPS Panel Full HD 2560x1440                        1\n",
       "Name: ScreenResolution, dtype: int64"
      ]
     },
     "execution_count": 4166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ScreenResolution'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Feature Engineering\n",
    "### leverages data to create new variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### - creating a new column Touchscreen with value 1 if touchscreen is available, else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4167,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['Touchscreen'] = df['ScreenResolution'].apply(lambda x:1 if 'Touchscreen' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4168,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "  <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",
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       "      <th>Weight</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>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",
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       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "    </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",
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       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0  \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0  \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0  \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0  \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0  "
      ]
     },
     "execution_count": 4168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4169,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>394</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 7500U 2.7GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>AMD Radeon R7 M445</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.33</td>\n",
       "      <td>41498.1936</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>Full HD / Touchscreen 1920x1080</td>\n",
       "      <td>Intel Core i7 7500U 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.10</td>\n",
       "      <td>93181.3920</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>15.6</td>\n",
       "      <td>IPS Panel 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 1050 Ti</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.50</td>\n",
       "      <td>66546.7200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Dell</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</td>\n",
       "      <td>Intel Core i3 6006U 2.0GHz</td>\n",
       "      <td>4</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>Intel HD Graphics 520</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.25</td>\n",
       "      <td>19127.5200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>534</th>\n",
       "      <td>Asus</td>\n",
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       "      <td>15.6</td>\n",
       "      <td>IPS Panel Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7300HQ 2.5GHz</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>2.30</td>\n",
       "      <td>87858.7200</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Company   TypeName  Inches                 ScreenResolution  \\\n",
       "394    Dell   Notebook    15.6                Full HD 1920x1080   \n",
       "384    Asus  Ultrabook    13.3  Full HD / Touchscreen 1920x1080   \n",
       "710  Lenovo     Gaming    15.6      IPS Panel Full HD 1920x1080   \n",
       "613    Dell   Notebook    15.6                         1366x768   \n",
       "534    Asus     Gaming    15.6      IPS Panel Full HD 1920x1080   \n",
       "\n",
       "                             Cpu  Ram                Memory  \\\n",
       "394   Intel Core i7 7500U 2.7GHz    8             256GB SSD   \n",
       "384   Intel Core i7 7500U 2.7GHz   16             512GB SSD   \n",
       "710  Intel Core i7 7700HQ 2.8GHz    8  128GB SSD +  1TB HDD   \n",
       "613   Intel Core i3 6006U 2.0GHz    4               1TB HDD   \n",
       "534  Intel Core i5 7300HQ 2.5GHz    8  128GB SSD +  1TB HDD   \n",
       "\n",
       "                            Gpu       OpSys  Weight       Price  Touchscreen  \n",
       "394          AMD Radeon R7 M445       Linux    2.33  41498.1936            0  \n",
       "384       Intel HD Graphics 620  Windows 10    1.10  93181.3920            1  \n",
       "710  Nvidia GeForce GTX 1050 Ti  Windows 10    2.50  66546.7200            0  \n",
       "613       Intel HD Graphics 520       Linux    2.25  19127.5200            0  \n",
       "534     Nvidia GeForce GTX 1060  Windows 10    2.30  87858.7200            0  "
      ]
     },
     "execution_count": 4169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4170,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Touchscreen'].value_counts().plot(kind='bar', color=['blue', 'green'],alpha=0.8)\n",
    "plt.title('Laptop: Touchscreen or Non-Touchscreen?', fontsize=14)\n",
    "legend_labels = ['No Touchscreen', 'Touchscreen']\n",
    "legend_handles = [plt.Rectangle((0,0),1,1, color='blue'), plt.Rectangle((0,0),1,1, color='green')]\n",
    "plt.legend(legend_handles, legend_labels, loc='upper right')\n",
    "plt.xticks(rotation=360)\n",
    "plt.xlabel('Touchscreen')\n",
    "plt.ylabel('Count')\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4171,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=df['Touchscreen'], y=df['Price'], hue=df['Touchscreen'], palette={0: 'blue', 1: 'green'},alpha=0.9)\n",
    "handles = ax.get_legend_handles_labels()[0]\n",
    "labels = ['No Touchscreen', 'Touchscreen']\n",
    "ax.legend(handles=handles, labels=labels)\n",
    "plt.title('Laptop Price by TouchScreen', fontsize=14)\n",
    "plt.tight_layout()\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### - creating a new column IPS with value 1 if IPS panel is available, else 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4172,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['Ips'] = df['ScreenResolution'].apply(lambda x:1 if 'IPS' in x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4173,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1  \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0  \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0  \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1  \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1  "
      ]
     },
     "execution_count": 4173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4174,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Ips'].value_counts().plot(kind='bar', color=['blue', 'green'],alpha=0.8)\n",
    "plt.xticks(ticks=[0, 1], labels=['Non-TouchScreen', 'TouchScreen'])\n",
    "legend_labels = ['Non Touchscreen', 'Touchscreen']\n",
    "legend_handles = [plt.Rectangle((0,0),1,1, color='blue'), plt.Rectangle((0,0),1,1, color='green')]\n",
    "plt.legend(legend_handles, legend_labels, loc='upper right')\n",
    "plt.title(\"IPS Count based on Touchscreen\",fontsize='14')\n",
    "plt.ylabel('IPS.count')\n",
    "plt.xlabel('Touchscreen')\n",
    "plt.xticks(rotation='horizontal')\n",
    "plt.tight_layout()\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4175,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x=df['Ips'], y=df['Price'], hue=df['Ips'],palette={0: 'blue', 1: 'green'},alpha=0.9)\n",
    "plt.xticks(ticks=[0, 1], labels=['Non-IPS', 'IPS'])\n",
    "handles = ax.get_legend_handles_labels()[0]\n",
    "labels = [ 'Other display','IPS display']\n",
    "ax.legend(handles=handles, labels=labels)\n",
    "plt.title('Laptop Price by IPS', fontsize=14)\n",
    "plt.xlabel('Touchscreen')\n",
    "plt.xlabel('Display Type')\n",
    "plt.tight_layout()\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### - Extracting X resolution and Y resolution from the ScreenResolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4176,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "new = df['ScreenResolution'].str.split('x',n=1,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4177,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>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>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Retina Display 2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>1440x900</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Retina Display 2880</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Retina Display 2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "                           X_res Y_res  \n",
       "0  IPS Panel Retina Display 2560  1600  \n",
       "1                           1440   900  \n",
       "2                   Full HD 1920  1080  \n",
       "3  IPS Panel Retina Display 2880  1800  \n",
       "4  IPS Panel Retina Display 2560  1600  "
      ]
     },
     "execution_count": 4177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['X_res'] = new[0]\n",
    "df['Y_res'] = new[1]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4178,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
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   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <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>1096</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 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>1TB HDD</td>\n",
       "      <td>AMD Radeon R7 M445</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.36</td>\n",
       "      <td>34035.2640</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>428</th>\n",
       "      <td>HP</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>12</td>\n",
       "      <td>256GB SSD +  1TB HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1070</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>3.35</td>\n",
       "      <td>106506.7200</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>530</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 1070</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>4.42</td>\n",
       "      <td>160520.3856</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>IPS Panel Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>1366x768</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>2.20</td>\n",
       "      <td>31808.1600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1366</td>\n",
       "      <td>768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i7 7700HQ 2.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1050 Ti</td>\n",
       "      <td>Linux</td>\n",
       "      <td>2.50</td>\n",
       "      <td>52161.1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Full HD 1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company  TypeName  Inches             ScreenResolution  \\\n",
       "1096    Dell  Notebook    15.6            Full HD 1920x1080   \n",
       "428       HP    Gaming    17.3            Full HD 1920x1080   \n",
       "530     Dell    Gaming    17.3  IPS Panel Full HD 1920x1080   \n",
       "209   Lenovo  Notebook    15.6                     1366x768   \n",
       "296     Acer  Notebook    15.6            Full HD 1920x1080   \n",
       "\n",
       "                              Cpu  Ram                Memory  \\\n",
       "1096   Intel Core i5 7200U 2.5GHz    8               1TB HDD   \n",
       "428   Intel Core i7 7700HQ 2.8GHz   12  256GB SSD +  1TB HDD   \n",
       "530   Intel Core i7 7700HQ 2.8GHz   16  128GB SSD +  1TB HDD   \n",
       "209    Intel Core i5 7200U 2.5GHz    8             256GB SSD   \n",
       "296   Intel Core i7 7700HQ 2.8GHz    8             256GB SSD   \n",
       "\n",
       "                             Gpu       OpSys  Weight        Price  \\\n",
       "1096          AMD Radeon R7 M445  Windows 10    2.36   34035.2640   \n",
       "428      Nvidia GeForce GTX 1070  Windows 10    3.35  106506.7200   \n",
       "530      Nvidia GeForce GTX 1070  Windows 10    4.42  160520.3856   \n",
       "209        Intel HD Graphics 620  Windows 10    2.20   31808.1600   \n",
       "296   Nvidia GeForce GTX 1050 Ti       Linux    2.50   52161.1200   \n",
       "\n",
       "      Touchscreen  Ips                   X_res Y_res  \n",
       "1096            0    0            Full HD 1920  1080  \n",
       "428             0    0            Full HD 1920  1080  \n",
       "530             0    1  IPS Panel Full HD 1920  1080  \n",
       "209             0    0                    1366   768  \n",
       "296             0    0            Full HD 1920  1080  "
      ]
     },
     "execution_count": 4178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4179,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['X_res'] = df['X_res'].str.replace(',','').str.findall(r'(\\d+\\.?\\d+)').apply(lambda x:x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4180,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Inches</th>\n",
       "      <th>ScreenResolution</th>\n",
       "      <th>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>X_res</th>\n",
       "      <th>Y_res</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <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>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1440</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>15.6</td>\n",
       "      <td>Full HD 1920x1080</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1920</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>15.4</td>\n",
       "      <td>IPS Panel Retina Display 2880x1800</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2880</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>13.3</td>\n",
       "      <td>IPS Panel Retina Display 2560x1600</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2560</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Inches                    ScreenResolution  \\\n",
       "0   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "1   Apple  Ultrabook    13.3                            1440x900   \n",
       "2      HP   Notebook    15.6                   Full HD 1920x1080   \n",
       "3   Apple  Ultrabook    15.4  IPS Panel Retina Display 2880x1800   \n",
       "4   Apple  Ultrabook    13.3  IPS Panel Retina Display 2560x1600   \n",
       "\n",
       "                          Cpu  Ram               Memory  \\\n",
       "0        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \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": 4180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4181,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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             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   object \n",
      " 14  Y_res             1303 non-null   object \n",
      "dtypes: float32(1), float64(2), int32(1), int64(2), object(9)\n",
      "memory usage: 142.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4182,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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             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": [
    "df['X_res'] = df['X_res'].astype('int')\n",
    "df['Y_res'] = df['Y_res'].astype('int')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4183,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          1.000000\n",
       "Touchscreen    0.191226\n",
       "Ips            0.252208\n",
       "X_res          0.556529\n",
       "Y_res          0.552809\n",
       "Name: Price, dtype: float64"
      ]
     },
     "execution_count": 4183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### - Adding new feature ppi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4184,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['ppi'] = (((df['X_res']**2) + (df['Y_res']**2))**0.5/df['Inches']).astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4185,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Inches         0.068197\n",
       "Ram            0.743007\n",
       "Weight         0.210370\n",
       "Price          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, dtype: float64"
      ]
     },
     "execution_count": 4185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4186,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['ScreenResolution'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4187,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>8</td>\n",
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      "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  Touchscreen  Ips  X_res  Y_res         ppi  \n",
       "0   71378.6832            0    1   2560   1600  226.983005  \n",
       "1   47895.5232            0    0   1440    900  127.677940  \n",
       "2   30636.0000            0    0   1920   1080  141.211998  \n",
       "3  135195.3360            0    1   2880   1800  220.534624  \n",
       "4   96095.8080            0    1   2560   1600  226.983005  "
      ]
     },
     "execution_count": 4187,
     "metadata": {},
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    "df.head()"
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  {
   "cell_type": "code",
   "execution_count": 4188,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <td>Apple</td>\n",
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       "      <td>AMD Radeon Pro 455</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi  \n",
       "0  226.983005  \n",
       "1  127.677940  \n",
       "2  141.211998  \n",
       "3  220.534624  \n",
       "4  226.983005  "
      ]
     },
     "execution_count": 4188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['Inches','X_res','Y_res'],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4189,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['Cpu'].value_counts()\n",
    "df['Cpu Name'] = df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4190,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi       Cpu Name  \n",
       "0  226.983005  Intel Core i5  \n",
       "1  127.677940  Intel Core i5  \n",
       "2  141.211998  Intel Core i5  \n",
       "3  220.534624  Intel Core i7  \n",
       "4  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 4190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### - Function to summarize different CPUs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4191,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def fetch_processor(text):\n",
    "    if text == 'Intel Core i7' or text == 'Intel Core i5' or text == 'Intel Core i3':\n",
    "        return text\n",
    "    else:\n",
    "        if text.split()[0] == 'Intel':\n",
    "            return 'Other Intel Processor'\n",
    "        else:\n",
    "            return 'AMD Processor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4192,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['Cpu brand'] = df['Cpu Name'].apply(fetch_processor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4193,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Cpu</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu Name</th>\n",
       "      <th>Cpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 2.3GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 1.8GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>Intel Core i5 7200U 2.5GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i7 2.7GHz</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>Intel Core i5 3.1GHz</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName                         Cpu  Ram               Memory  \\\n",
       "0   Apple  Ultrabook        Intel Core i5 2.3GHz    8            128GB SSD   \n",
       "1   Apple  Ultrabook        Intel Core i5 1.8GHz    8  128GB Flash Storage   \n",
       "2      HP   Notebook  Intel Core i5 7200U 2.5GHz    8            256GB SSD   \n",
       "3   Apple  Ultrabook        Intel Core i7 2.7GHz   16            512GB SSD   \n",
       "4   Apple  Ultrabook        Intel Core i5 3.1GHz    8            256GB SSD   \n",
       "\n",
       "                            Gpu  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0  Intel Iris Plus Graphics 640  macOS    1.37   71378.6832            0    1   \n",
       "1        Intel HD Graphics 6000  macOS    1.34   47895.5232            0    0   \n",
       "2         Intel HD Graphics 620  No OS    1.86   30636.0000            0    0   \n",
       "3            AMD Radeon Pro 455  macOS    1.83  135195.3360            0    1   \n",
       "4  Intel Iris Plus Graphics 650  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi       Cpu Name      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": 4193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4194,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "cpu_counts = df['Cpu brand'].value_counts()\n",
    "cpu_counts.plot(kind='bar', color=sns.color_palette(\"husl\", 9))\n",
    "\n",
    "plt.title('CPU Brand Counts', fontsize=14)\n",
    "plt.xlabel('CPU Brand', fontsize=12,fontweight='bold')\n",
    "plt.ylabel('Brand.Count', fontsize=12,fontweight='bold')\n",
    "\n",
    "for index, value in enumerate(cpu_counts):\n",
    "    plt.text(index, value + 2, str(value), ha='center', fontsize=10)\n",
    "\n",
    "sns.despine()\n",
    "plt.xticks(rotation=0)\n",
    "plt.tight_layout()\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4195,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.set(style=\"whitegrid\")\n",
    "sns.barplot(x=df['Cpu brand'],y=df['Price'],palette=sns.color_palette(\"husl\", 5))\n",
    "plt.xticks(rotation='horizontal')\n",
    "plt.title('CPU Brand distribution Prices', fontsize=14)\n",
    "plt.xlabel('CPU Brand', fontsize=12,fontweight='bold')\n",
    "plt.ylabel('Brand.Price', fontsize=12,fontweight='bold')\n",
    "sns.despine()\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4196,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['Cpu','Cpu Name'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4197,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>Touchscreen</th>\n",
       "      <th>Ips</th>\n",
       "      <th>ppi</th>\n",
       "      <th>Cpu brand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 640</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>128GB Flash Storage</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>512GB SSD</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>256GB SSD</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram               Memory                           Gpu  \\\n",
       "0   Apple  Ultrabook    8            128GB SSD  Intel Iris Plus Graphics 640   \n",
       "1   Apple  Ultrabook    8  128GB Flash Storage        Intel HD Graphics 6000   \n",
       "2      HP   Notebook    8            256GB SSD         Intel HD Graphics 620   \n",
       "3   Apple  Ultrabook   16            512GB SSD            AMD Radeon Pro 455   \n",
       "4   Apple  Ultrabook    8            256GB SSD  Intel Iris Plus Graphics 650   \n",
       "\n",
       "   OpSys  Weight        Price  Touchscreen  Ips         ppi      Cpu brand  \n",
       "0  macOS    1.37   71378.6832            0    1  226.983005  Intel Core i5  \n",
       "1  macOS    1.34   47895.5232            0    0  127.677940  Intel Core i5  \n",
       "2  No OS    1.86   30636.0000            0    0  141.211998  Intel Core i5  \n",
       "3  macOS    1.83  135195.3360            0    1  220.534624  Intel Core i7  \n",
       "4  macOS    1.37   96095.8080            0    1  226.983005  Intel Core i5  "
      ]
     },
     "execution_count": 4197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4198,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ram_value_counts = df['Ram'].value_counts()\n",
    "ram_value_counts.plot(kind='bar',width=0.9, color=sns.color_palette(\"husl\", 9))\n",
    "\n",
    "plt.title('RAM Distribution', fontsize=14)\n",
    "plt.xlabel('RAM Size')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xticks(rotation='horizontal')\n",
    "\n",
    "for i, count in enumerate(ram_value_counts):\n",
    "    plt.text(i, count + 1, str(count), ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.show(\"png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4199,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"whitegrid\")\n",
    "sns.barplot(x=df['Ram'], y=df['Price'], palette=\"viridis\")\n",
    "\n",
    "plt.title('RAM Distribution by Price', fontsize=14)\n",
    "plt.xlabel('RAM Size', fontsize=12)\n",
    "plt.ylabel('Price', fontsize=12)\n",
    "plt.xticks(rotation='horizontal')\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4200,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
       "1TB SSD                           14\n",
       "512GB SSD +  1TB HDD              14\n",
       "256GB SSD +  2TB HDD              10\n",
       "1.0TB Hybrid                       9\n",
       "256GB Flash Storage                8\n",
       "16GB Flash Storage                 7\n",
       "32GB SSD                           6\n",
       "180GB SSD                          5\n",
       "128GB Flash Storage                4\n",
       "512GB SSD +  2TB HDD               3\n",
       "16GB SSD                           3\n",
       "1TB SSD +  1TB HDD                 2\n",
       "256GB SSD +  500GB HDD             2\n",
       "512GB Flash Storage                2\n",
       "256GB SSD +  256GB SSD             2\n",
       "128GB SSD +  2TB HDD               2\n",
       "32GB HDD                           1\n",
       "512GB SSD +  256GB SSD             1\n",
       "1.0TB HDD                          1\n",
       "508GB Hybrid                       1\n",
       "64GB SSD                           1\n",
       "1TB HDD +  1TB HDD                 1\n",
       "512GB SSD +  1.0TB Hybrid          1\n",
       "240GB SSD                          1\n",
       "256GB SSD +  1.0TB Hybrid          1\n",
       "128GB HDD                          1\n",
       "512GB SSD +  512GB SSD             1\n",
       "64GB Flash Storage +  1TB HDD      1\n",
       "8GB SSD                            1\n",
       "Name: Memory, dtype: int64"
      ]
     },
     "execution_count": 4200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Memory'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### - Transfrom Memory to HDD, SDD, FLASH and HYBRID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4201,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python37\\lib\\site-packages\\ipykernel_launcher.py:16: FutureWarning:\n",
      "\n",
      "The default value of regex will change from True to False in a future version.\n",
      "\n",
      "c:\\python37\\lib\\site-packages\\ipykernel_launcher.py:25: FutureWarning:\n",
      "\n",
      "The default value of regex will change from True to False in a future version.\n",
      "\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",
    "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",
    "df.drop(columns=['first', 'second', 'Layer1HDD', 'Layer1SSD', 'Layer1Hybrid',\n",
    "       'Layer1Flash_Storage', 'Layer2HDD', 'Layer2SSD', 'Layer2Hybrid',\n",
    "       'Layer2Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4202,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>Memory</th>\n",
       "      <th>Gpu</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>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>691</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>256 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.70</td>\n",
       "      <td>37242.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>157.350512</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>389</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>16</td>\n",
       "      <td>512 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.42</td>\n",
       "      <td>133679.52</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>209.800683</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>1169</th>\n",
       "      <td>MSI</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>8</td>\n",
       "      <td>1000 HDD</td>\n",
       "      <td>Nvidia GeForce GTX 1050</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>2.20</td>\n",
       "      <td>44701.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>Razer</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>1000 SSD</td>\n",
       "      <td>Nvidia GeForce GTX 1060</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.95</td>\n",
       "      <td>186426.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>157.350512</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>Toshiba</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>32</td>\n",
       "      <td>512 SSD</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
       "      <td>Windows 10</td>\n",
       "      <td>1.05</td>\n",
       "      <td>149130.72</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.632118</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Company            TypeName  Ram    Memory                      Gpu  \\\n",
       "691    Lenovo            Notebook    4   256 SSD    Intel HD Graphics 620   \n",
       "389    Lenovo  2 in 1 Convertible   16   512 SSD    Intel HD Graphics 620   \n",
       "1169      MSI              Gaming    8  1000 HDD  Nvidia GeForce GTX 1050   \n",
       "1231    Razer              Gaming   16  1000 SSD  Nvidia GeForce GTX 1060   \n",
       "491   Toshiba           Ultrabook   32   512 SSD    Intel HD Graphics 620   \n",
       "\n",
       "           OpSys  Weight      Price  Touchscreen  Ips         ppi  \\\n",
       "691   Windows 10    1.70   37242.72            0    0  157.350512   \n",
       "389   Windows 10    1.42  133679.52            1    0  209.800683   \n",
       "1169  Windows 10    2.20   44701.92            0    1  141.211998   \n",
       "1231  Windows 10    1.95  186426.72            0    0  157.350512   \n",
       "491   Windows 10    1.05  149130.72            1    0  165.632118   \n",
       "\n",
       "          Cpu brand   HDD   SSD  Hybrid  Flash_Storage  \n",
       "691   Intel Core i5     0   256       0              0  \n",
       "389   Intel Core i7     0   512       0              0  \n",
       "1169  Intel Core i7  1000     0       0              0  \n",
       "1231  Intel Core i7     0  1000       0              0  \n",
       "491   Intel Core i7     0   512       0              0  "
      ]
     },
     "execution_count": 4202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4203,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['Memory'],inplace=True)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4204,
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       "      <th>2</th>\n",
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       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 620</td>\n",
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       "      <td>Intel Iris Plus Graphics 650</td>\n",
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      "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  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  Hybrid  \\\n",
       "0   71378.6832            0    1  226.983005  Intel Core i5    0  128       0   \n",
       "1   47895.5232            0    0  127.677940  Intel Core i5    0    0       0   \n",
       "2   30636.0000            0    0  141.211998  Intel Core i5    0  256       0   \n",
       "3  135195.3360            0    1  220.534624  Intel Core i7    0  512       0   \n",
       "4   96095.8080            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": 4204,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4205,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram              0.743007\n",
       "Weight           0.210370\n",
       "Price            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, dtype: float64"
      ]
     },
     "execution_count": 4205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4206,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['Hybrid','Flash_Storage'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4207,
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      "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  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  \n",
       "0   71378.6832            0    1  226.983005  Intel Core i5    0  128  \n",
       "1   47895.5232            0    0  127.677940  Intel Core i5    0    0  \n",
       "2   30636.0000            0    0  141.211998  Intel Core i5    0  256  \n",
       "3  135195.3360            0    1  220.534624  Intel Core i7    0  512  \n",
       "4   96095.8080            0    1  226.983005  Intel Core i5    0  256  "
      ]
     },
     "execution_count": 4207,
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    }
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4208,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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 430            1\n",
       "AMD Radeon R7 M465           1\n",
       "AMD R17M-M1-70               1\n",
       "Intel HD Graphics 530        1\n",
       "Intel Iris Graphics 550      1\n",
       "Name: Gpu, Length: 110, dtype: int64"
      ]
     },
     "execution_count": 4208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### Extracting Brand Name from GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4209,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
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       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel HD Graphics 6000</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>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>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>AMD Radeon Pro 455</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>Intel Iris Plus Graphics 650</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram                           Gpu  OpSys  Weight  \\\n",
       "0   Apple  Ultrabook    8  Intel Iris Plus Graphics 640  macOS    1.37   \n",
       "1   Apple  Ultrabook    8        Intel HD Graphics 6000  macOS    1.34   \n",
       "2      HP   Notebook    8         Intel HD Graphics 620  No OS    1.86   \n",
       "3   Apple  Ultrabook   16            AMD Radeon Pro 455  macOS    1.83   \n",
       "4   Apple  Ultrabook    8  Intel Iris Plus Graphics 650  macOS    1.37   \n",
       "\n",
       "         Price  Touchscreen  Ips         ppi      Cpu brand  HDD  SSD  \\\n",
       "0   71378.6832            0    1  226.983005  Intel Core i5    0  128   \n",
       "1   47895.5232            0    0  127.677940  Intel Core i5    0    0   \n",
       "2   30636.0000            0    0  141.211998  Intel Core i5    0  256   \n",
       "3  135195.3360            0    1  220.534624  Intel Core i7    0  512   \n",
       "4   96095.8080            0    1  226.983005  Intel Core i5    0  256   \n",
       "\n",
       "  Gpu brand  \n",
       "0     Intel  \n",
       "1     Intel  \n",
       "2     Intel  \n",
       "3       AMD  \n",
       "4     Intel  "
      ]
     },
     "execution_count": 4209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'] = df['Gpu'].apply(lambda x:x.split()[0])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4210,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "ARM         1\n",
       "Name: Gpu brand, dtype: int64"
      ]
     },
     "execution_count": 4210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4211,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df = df[df['Gpu brand'] != 'ARM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4212,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intel     722\n",
       "Nvidia    400\n",
       "AMD       180\n",
       "Name: Gpu brand, dtype: int64"
      ]
     },
     "execution_count": 4212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gpu brand'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4213,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xTGEiIiKGKUxERMQwhYmIiBimMBEREcMUJiIiYpjCREREDFOYiIhcAl7mJm4/rzYKExGRS6BJ9xhMQTfTpHtMfZdSL3Q7FRGRS8B8QyjmG0Lru4x6oyMTERExzKNh8v777xMXF0dUVBSzZ88GYNu2bcTGxtKvXz8WLlzo6rtv3z4GDx5MZGQkU6dOpbS0FICTJ08SHx9PVFQUY8aMoaioCICCggIee+wxoqOjiY+PJzc315NDERGRWngsTI4dO8aMGTNITU3l7bffZu/evWzZsoUpU6aQmpqKzWYjKyuLLVu2ADB58mSmTZtGZmYmTqeTtWvXAjBz5kyGDh2K3W4nNDSU1NRUABYtWoTVaiUjI4MhQ4YwZ84cTw1FRETOw2Nh8q9//Yv+/fvTqlUrzGYzCxcupGnTpoSEhNCmTRtMJhOxsbHY7XZOnDjBuXPn6NGjBwBxcXHY7XZKSkrYuXMnkZGRbu0AmzdvJjY2FoABAwawdetWSkpKPDUcERGphcdOwB85cgSz2czIkSPJzc2lT58+3HzzzQQGBrr6WCwWsrOzycnJcWsPDAwkOzub/Px8fH19MZlMbu2A2zomkwlfX1/y8vIICgqqU31ZWVmXaqgi4iFhYWH1XcJVYffu3Ya34bEwKSsrY9euXaxcuZJmzZrxhz/8gaZNm1bp5+XlhdPpvKD2mjRqVPcDrdDQUHx8fOrcX0TkSlWX0HY4HLW+CffYNNd1111Hr169CAgIoEmTJtxzzz18/PHHnDp1ytUnJycHi8VCUFCQW3tubi4Wi4WAgAAKCwspKytza4eKo5rKdUpLSyksLMTf399TwxERkVp4LEz69OnDRx99REFBAWVlZXz44YdERUVx6NAhjhw5QllZGZs2bSI8PJzg4GB8fHxch1rp6emEh4djNpuxWq3YbDa3doCIiAjS09MBsNlsWK1WzGazp4YjIiK18Ng0V/fu3fn973/P0KFDKSkp4Te/+Q0PP/wwN910E2PHjsXhcBAREUFUVBQAKSkpJCcnU1RURJcuXUhMTARgxowZJCUlsWTJElq3bs2CBQsAGD9+PElJScTExODn50dKSoqnhiIiIufh5azuxMQVrHLeT+dMRBqGwcv31XcJV7Q3h3euU7/zvXbqCngRETFMYSIiIoYpTERExDCFiYiIGKYwERERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSIiIoYpTERExDCFiYiIGKYwERERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSIiIoYpTERExDCFyRXm008/5amnnuLTTz+t71JE5Cpi8uTGExMT+eGHHzCZKp5m1qxZHD16lCVLllBSUsLw4cOJj48HYNu2bcydOxeHw0F0dDQTJ04EYN++fSQnJ1NYWIjVamXmzJmYTCZOnjzJ5MmT+eGHH2jXrh0pKSk0b97ck8NpEJYvX84333zDmTNnuOOOO+q7HBG5SnjsyMTpdHLw4EE2bNjg+teqVSsWLlzIa6+9xoYNG1izZg3ffvst586dY8qUKaSmpmKz2cjKymLLli0ATJ48mWnTppGZmYnT6WTt2rUAzJw5k6FDh2K32wkNDSU1NdVTQ2lQzpw54/ZTRORy8FiYHDx4EC8vL0aNGsXAgQNZtWoV27Zt44477sDf359mzZoRGRmJ3W7niy++ICQkhDZt2mAymYiNjcVut3PixAnOnTtHjx49AIiLi8Nut1NSUsLOnTuJjIx0axcRkfrhsTApKCigV69evPTSSyxfvpw33niDkydPEhgY6OpjsVjIzs4mJyenTu2BgYFkZ2eTn5+Pr6+va/qssl1EROqHx86Z3Hrrrdx6660ANGvWjAcffJC5c+cyevRot35eXl44nc4q619M+4XIysq6oP4NhcPhcP3cvXt3PVcjYkxYWFh9l3BVuBSvFR4Lk127dlFSUkKvXr2AinMowcHBnDp1ytUnJycHi8VCUFBQndpzc3OxWCwEBARQWFhIWVkZ3t7ervYLERoaio+Pj8FR/vJUjsnHx0d/iCJSJ3V5rXA4HLW+CffYNNfp06eZP38+DoeDwsJC3nrrLf7617/yySefkJeXx9mzZ3n33XcJDw+ne/fuHDp0iCNHjlBWVsamTZsIDw8nODgYHx8fV2qmp6cTHh6O2WzGarVis9nc2kVEpH547MikT58+7Nmzh/vvv5/y8nKGDh1KWFgYEydOJDExkZKSEh588EG6desGwLx58xg7diwOh4OIiAiioqIASElJITk5maKiIrp06UJiYiIAM2bMICkpiSVLltC6dWsWLFjgqaGIiMh5eDmrOwFxBas8VLtSp7kSExM5ceIEwcHBrFixor7LETFs8PJ99V3CFe3N4Z3r1O98r526Al5ERAxTmNSBs7S0vku44ul3LNKwefR2KlcKL5OJ72a9WN9l1ElZ3n9dPxtKzQCtpz9Z3yWIiAE6MhEREcMUJiIiYpjCREREDFOYiIiIYQoTERExTGEiIiKGKUxERMQwhYmIiBimMLnCNPE2u/0UEbkcFCZXmME3dqZzy+sYfGPdbt4mInIp6HYqV5hbr23Frde2qu8yROQqoyMTERExTGEiIiKGKUxERMQwhYmIiBimMBEREcMUJiIiYpjCREREDKtzmGzZsoVZs2Zx9OhR7HZ7nZ/gueeeIykpCYB9+/YxePBgIiMjmTp1KqX/+73fJ0+eJD4+nqioKMaMGUNRUREABQUFPPbYY0RHRxMfH09ubi4AxcXFTJ48mejoaB544AEOHDhQ53pEROTSq1OYLF++nMcff5zXX3+d48ePM2HCBF544YXzrvfJJ5/w1ltvuR5PnjyZadOmkZmZidPpZO3atQDMnDmToUOHYrfbCQ0NJTU1FYBFixZhtVrJyMhgyJAhzJkzB4CVK1fStGlTMjIymDJliiusRESkftQpTFatWsXYsWMBaNq0KcOGDWPNmjW1rvPf//6XhQsXMnr0aABOnDjBuXPn6NGjBwBxcXHY7XZKSkrYuXMnkZGRbu0AmzdvJjY2FoABAwawdetWSkpK2Lx5MwMHDgTg9ttvJz8/n5MnT17g0EVE5FKp0+1U8vPzueGGGwDw9vamU6dOpKen17rO9OnTmThxIt999x0AOTk5BAYGupYHBgaSnZ1Nfn4+vr6+mEwmt/afr2MymfD19SUvL6/abX3//fdcf/31dRw2ZGVl1blvWFhYnfvKxdu9e3d9l1Dv9u3bx5YtW4iIiKBzZ91fTX97l8el+NurU5j8+te/5uWXXwYgNTWV3bt307Nnzxr7r1u3jtatW9OrVy/S0tIAcDqdVfp5eXnV2F6TRo2qP5iqqb0moaGh+Pj4XNA64ll64YC///3vHDx4EG9vbx555JH6LkeuEnX523M4HLW+Ca9TmMyYMYPJkydz6NAhNm/eTI8ePZgxY0aN/W02G7m5uQwaNIgff/yRM2fO4OXlxalTp1x9cnNzsVgsBAQEUFhYSFlZGd7e3q52AIvFwqlTp2jVqhWlpaUUFhbi7++PxWIhNzeXkJAQt22JNHRnzpxx+ynSUNQpTIKCgnjllVcoLS11veg3bdq0xv7Lli1z/T8tLY0dO3Ywd+5cBgwYwO7duwkLCyM9PZ3w8HDMZjNWqxWbzUZsbKyrHSAiIoL09HRGjx6NzWbDarViNpuJiIhgw4YNWK1Wdu3ahY+PzwVNcYmIyKVVp7mhnTt3ct9999G0aVOOHTtGeHg4u3btuuAnS0lJYe7cuURHR3P27FkSExOBiiOftWvX0r9/f3bt2sWECRMAGD9+PJ9//jkxMTG89tprTJ8+HYCEhASKi4uJiYlhzpw5zJ8//4JrERGRS6dORyZz5syhefPmnDlzBj8/P9q0acNf/vIX1/mQ2sTFxREXFwdAp06dWL9+fZU+wcHBrFy5skq7v78/S5curdLu4+PDc889V5fSRUTkMqjTkcmhQ4cYN24czZo144YbbuDxxx/n4MGDnq5NREQaiDqFSatWrXj33XfJzs7m+PHjbNq0iaCgIE/XJiIiDUSdprlGjRpFcnIyNpvN1VZ5NbqIiEidwuTBBx+kTZs2bN68GS8vL+6+++5arzMREZGrS61hUlhYSLNmzThz5gxdu3ala9eubst8fX09XqCIiPzy1Romt99+O6+++iojRoyoclW6l5cXe/fu9WhxIiLSMNQaJvfffz8Wi4UHHnjgctUjckkVlxXT2LtxfZdxRdPvWOA8YTJ37lwA2rRpQ2xsLG3atLksRYlcKo29G/Pwir71XUad/XAawIvvTx9vMHW/nvh+fZcgvwB1+mjwq6++yr59+zxdi4iINFB1+jRXnz59+Nvf/saPP/7INddc4zp/cs8993i0OBERaRjqFCZvv/02UPEdIJW3jffy8tLRioiIAHUIkyNHjjBp0iS8vLy49tprL0dNIiLSwNQaJm+99RbJycmUl5cDMGbMGMaNG3dZChMRkYaj1hPwixcvpm3btkydOhWr1eo6byIiIvJTtYZJTk4O48eP55FHHiElJYXS0lLXd7qLyKXnZXL/KdJQ1BompaWltGzZEqj4bhGA4uJijxclcrVqfiuYWzlpfmt9VyJyYc77/uf1119n8+bNlJWV4eXlxT//+U+uu+46vLy8SEpKuhw1ilw1fG6o+CfS0Jw3TN599123x++88w6AwkRERFxqDZN///vfl6sOERFpwGoNk+Dg4MtVh4iINGB1ujfXxXrhhRfo378/MTExLFu2DIBt27YRGxtLv379WLhwoavvvn37GDx4MJGRkUydOpXS0lIATp48SXx8PFFRUYwZM4aioiIACgoKeOyxx4iOjiY+Pp7c3FxPDkVERGrhsTDZsWMHn376KRs3buTNN99k5cqV/Oc//2HKlCmkpqZis9nIyspiy5YtAEyePJlp06aRmZmJ0+lk7dq1AMycOZOhQ4dit9sJDQ0lNTUVgEWLFmG1WsnIyGDIkCH6GmERkXrksTDp2bMnK1aswGQy8cMPP1BWVkZBQQEhISG0adMGk8lEbGwsdrudEydOcO7cOXr06AFAXFwcdrudkpISdu7cSWRkpFs7wObNm4mNjQVgwIABbN26lZKSEk8NR0REauHRaS6z2cz//M//EBMTQ69evcjJySEwMNC13GKxkJ2dXaU9MDCQ7Oxs8vPz8fX1xWQyubUDbuuYTCZ8fX3Jy8vz5HBERKQGHr/Odty4cYwaNYrRo0dz+PDhKssr70J8Ie01adSo7tmYlZVV575hYWF17isXb/fu3Zd8m9p3l4cn9h1o/10ul2L/eSxMDhw4QHFxMZ07d6Zp06b069cPu92Ot7e3q09OTg4Wi4WgoCBOnTrlas/NzcVisRAQEEBhYSFlZWV4e3u72qHiqObUqVO0atWK0tJSCgsLXVfp10VoaCg+Pj6XbLxinF44Gi7tu4atLvvP4XDU+ibcY9Ncx48fJzk5meLiYoqLi/n3v//NQw89xKFDhzhy5AhlZWVs2rSJ8PBwgoOD8fHxcaVjeno64eHhmM1mrFYrNpvNrR0gIiKC9PR0AGw2G1arFbPZ7KnhiIhILTx2ZBIREcGePXu4//778fb2pl+/fsTExBAQEMDYsWNxOBxEREQQFRUFQEpKCsnJyRQVFdGlSxcSExMBmDFjBklJSSxZsoTWrVuzYMECAMaPH09SUhIxMTH4+fmRkpLiqaGIiMh5eDmrOzFxBas8VLvQaa7vZr3owaqk9fQnPbbth1f09di2BV5PfN+j2x+8XN/o6klvDu9cp37ne+306Ke5RETk6qAwERERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSIiIoYpTERExDCFiYiIGKYwERERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSIiIoYpTERExDCFiYiIGKYwERERwxQmIiJimMJEREQMU5iIiIhhHg2TF198kZiYGGJiYpg/fz4A27ZtIzY2ln79+rFw4UJX33379jF48GAiIyOZOnUqpaWlAJw8eZL4+HiioqIYM2YMRUVFABQUFPDYY48RHR1NfHw8ubm5nhyKiIjUwmNhsm3bNj766CPeeust0tPT+eqrr9i0aRNTpkwhNTUVm81GVlYWW7ZsAWDy5MlMmzaNzMxMnE4na9euBWDmzJkMHToUu91OaGgoqampACxatAir1UpGRgZDhgxhzpw5nhqKiIich8fCJDAwkKSkJBo3bozZbKZ9+/YcPnyYkJAQ2rRpg8lkIjY2FrvdzokTJzh37hw9evQAIC4uDrvdTklJCTt37iQyMtKtHWDz5s3ExsYCMGDAALZu3UpJSYmnhiMiIrUweWrDN998s+v/hw8fxmazkZCQQGBgoKvdYrGQnZ1NTk6OW3tgYCDZ2dnk5+fj6+uLyWRyawfc1jGZTPj6+pKXl0dQUFCd6svKyqrzWMLCwurcVy7e7t27L/k2te8uD0/sO9D+u1wuxf7zWJhU+uabb3j88cf505/+hMlk4tChQ27Lvby8cDqdVdarrb0mjRrV/UArNDQUHx+fOvcXz9MLR8Olfdew1WX/ORyOWt+Ee/QE/O7duxk+fDiTJk3igQceICgoiFOnTrmW5+TkYLFYqrTn5uZisVgICAigsLCQsrIyt3aoOKqpXKe0tJTCwkL8/f09ORwREamBx8Lku+++44knniAlJYWYmBgAunfvzqFDhzhy5AhlZWVs2rSJ8PBwgoOD8fHxcR1qpaenEx4ejtlsxmq1YrPZ3NoBIiIiSE9PB8Bms2G1WjGbzZ4ajoiI1MJj01yvvPIKDoeDefPmudoeeugh5s2bx9ixY3E4HERERBAVFQVASkoKycnJFBUV0aVLFxITEwGYMWMGSUlJLFmyhNatW7NgwQIAxo8fT1JSEjExMfj5+ZGSkuKpoYiIyHl4LEySk5NJTk6udtnGjRurtHXq1In169dXaQ8ODmblypVV2v39/Vm6dKnxQkVExDBdAS8iIoYpTERExDCFiYiIGKYwERERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSIiIoYpTERExDCFiYiIGKYwERERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSIiIoYpTERExDCFiYiIGKYwERERwzweJoWFhQwYMIDjx48DsG3bNmJjY+nXrx8LFy509du3bx+DBw8mMjKSqVOnUlpaCsDJkyeJj48nKiqKMWPGUFRUBEBBQQGPPfYY0dHRxMfHk5ub6+mhiIhIDTwaJnv27OHhhx/m8OHDAJw7d44pU6aQmpqKzWYjKyuLLVu2ADB58mSmTZtGZmYmTqeTtWvXAjBz5kyGDh2K3W4nNDSU1NRUABYtWoTVaiUjI4MhQ4YwZ84cTw5FRERq4dEwWbt2LTNmzMBisQDwxRdfEBISQps2bTCZTMTGxmK32zlx4gTnzp2jR48eAMTFxWG32ykpKWHnzp1ERka6tQNs3ryZ2NhYAAYMGMDWrVspKSnx5HBERKQGJk9u/OdHCzk5OQQGBroeWywWsrOzq7QHBgaSnZ1Nfn4+vr6+mEwmt/afb8tkMuHr60teXh5BQUGeHJKIiFTDo2Hyc06ns0qbl5fXBbfXpFGjuh9oZWVl1blvWFhYnfvKxdu9e/cl36b23eXhiX0H2n+Xy6XYf5c1TIKCgjh16pTrcU5ODhaLpUp7bm4uFouFgIAACgsLKSsrw9vb29UOFUc1p06dolWrVpSWllJYWIi/v3+dawkNDcXHx+eSjU2M0wtHw6V917DVZf85HI5a34Rf1o8Gd+/enUOHDnHkyBHKysrYtGkT4eHhBAcH4+Pj40rH9PR0wsPDMZvNWK1WbDabWztAREQE6enpANhsNqxWK2az+XIOR0RE/tdlPTLx8fFh3rx5jB07FofDQUREBFFRUQCkpKSQnJxMUVERXbp0ITExEYAZM2aQlJTEkiVLaN26NQsWLABg/PjxJCUlERMTg5+fHykpKZdzKCIi8hOXJUzef/991/979erFxo0bq/Tp1KkT69evr9IeHBzMypUrq7T7+/uzdOnSS1uoiIhcFF0BLyIihilMRETEMIWJiIgYpjARERHDFCYiImKYwkRERAxTmIiIiGEKExERMUxhIiIihilMRETEMIWJiIgYpjARERHDFCYiImKYwkRERAxTmIiIiGEKExERMUxhIiIihilMRETEMIWJiIgYpjARERHDFCYiImJYgw6Tt99+m/79+3PfffexevXq+i5HROSqZarvAi5WdnY2CxcuJC0tjcaNG/PQQw/x61//ml/96lf1XZqIyFWnwYbJtm3buOOOO/D39wcgMjISu93Ok08+Wet6TqcTgOLi4gt6vtKmPhdVp9SNw+Hw2LZbmK/x2LbFs/sOoKW53KPbv9rVdf9VvmZWvob+XIMNk5ycHAIDA12PLRYLX3zxxXnXKykpAWD//v0X9oR9b7uw/nJBcrKyPLbtxzpM8di2BbI8uO8Anujk0c1f9S50/5WUlNCkSZMq7Q02TKpLRy8vr/Ou17x5czp06IDZbK5TfxERqXjNLSkpoXnz5tUub7BhEhQUxK5du1yPc3JysFgs512vUaNG+Pn5ebI0EZErUnVHJJUa7Ke57rzzTj755BPy8vI4e/Ys7777LuHh4fVdlojIValBH5lMnDiRxMRESkpKePDBB+nWrVt9lyUiclXyctZ0al5ERKSOGuw0l4iI/HIoTERExDCFiYiIGKYwERERwxQmDUzHjh3P2ychIeG8ffr27cvx48cvRUlyAfbv30/Hjh3JzMx0tSUkJHDbbbdVucXPoEGDXPty8eLF/OY3v2HQoEEMHDiQ6Oho/v73v1/W2q8mx48fp2PHjnz88cdu7bX93UydOpUvv/yySntSUhJpaWlkZ2czatQoj9T7S6AwuQLt2LGjvkuQGqSlpREZGckbb7zh1u7n58dHH33kenzw4EFycnLc+jz00ENs2LCBjRs3snr1ajZu3Mi6desuS91XI7PZzLRp0ygsLKxT/zlz5nDLLbfUuDwoKOiKfgOgMGmgtm/fzqOPPsof/vAHIiMjGTduHMXFxcyePRuAIUOGALB161YefPBB7r//fp588kny8/Prs+yrWmlpKRs3bmTixIns3buXo0ePupb169fP7WjFZrMRGRlZ47YCAgIYM2YMr732mkdrvppZLBbuvPNOnnvuObf2efPmYbfbXY/j4uL46quvSEhIYPv27TidTubOnUtkZCQJCQmu/Xz8+HH69u0LVByhJiQkMHjwYPr06cOKFSsu38A8RGHSgH322WdMnz6djIwMTp48yUcffURycjIA69atIy8vj+eff55XXnmF9PR07rrrLlJSUuq56qvX5s2buf7662nXrh333nuv29FJ79692bFjh+tGpJs3b6ZPnz61bq9Dhw4cPHjQozVf7ZKSkvjoo4/cprusVis2mw2Aw4cP43A46Nq1q2t5ZmYme/fuZdOmTbzwwgtubxoqrVu3jj/84Q+8+eabrFixgoULF3p+MB6mMGnAbr75Zlq1akWjRo1o3749P/74o9vyPXv28N1335GYmMigQYNYvXo1R44cqadqJS0tjQEDBgDQv39/3nrrLdd5Eh8fH8LCwti2bRv79++nTZs2td4HCSpubHq+PmKMr68vf/7zn92muyIiIvj8888pLCxk06ZNxMbGuq2zY8cO+vXrh9lsJiAgoNrbPCUlJeFwOHj55ZdZuHAhZ86cuSzj8aQGezsVqXgBquTl5VXlTsplZWXcdtttLF26FKj43oKioqLLWqNU+OGHH9i6dStZWVmsWLECp9NJQUEB7777rqtPVFQUmZmZBAUF0b9///Nu8+uvv6Z9+/aeLFuAu+66y226y2w2c/fdd/P+++9jt9t5+eWX3fp7eXlRXv5/38FiMlV9mZ0wYQItWrSgT58+9O/fn3feecezg7gMdGRyBfL29qa0tJTu3bvz+eefc+jQIQBSU1OZP39+PVd3ddq4cSN33HEHW7du5f333+eDDz5g9OjRrFmzxtUnPDyc7du3s3Xr1vPetDQnJ4elS5cSHx/v6dKF/5vuqvxQxKBBg1i2bBktW7YkODjYrW+vXr2w2+0UFxfz448/8uGHH1bZ3scff8y4ceO499572blzJ1Dx5q8h05HJFeiee+5h0KBBpKWl8Ze//IUJEyZQXl5OUFAQf/3rX+u7vKtSWloaEydOdGsbOnQo//jHP/D19QWgcePG3HZbxZew/fSos9Ibb7zBe++95zoK/d3vfkdMTIznixfXdNfIkSMBCAsL4/Tp0zz00ENV+t577718+eWXDBgwgOuuu67ao8exY8cydOhQWrRoQbt27QgODub48eOEhIR4fCyeohs9ioiIYZrmEhERwxQmIiJimMJEREQMU5iIiIhhChMRETFMYSJSDafTybp16xg0aBDdunWjZ8+eDB8+nN27d7v6JCQk0LFjRzp27EinTp0ICwtj8uTJnD17Fqi4w+ygQYPctpuQkIDVaq32Ofv27evaXseOHenWrRsPP/ww3377recG+r/mzJlDx44ddSdpuWgKE5FqPP/88yQnJ3PTTTcxf/58kpKSOHr0KCNGjODAgQOufs2aNWPZsmUsXbqUfv36sXHjxipXRF+Itm3bsmzZMl599VWmT5/Ol19+yTPPPHMphiTiUbpoUeRnsrOzefXVV+ndu7fbDfg6deqEzWZzu1LZ29ubO++8E6i4AWBaWhpZWVkX/dxNmzYlLCwMp9PJkSNHaN68Oddddx1QceHjM888Q2xsLB988AEjRozgrrvuYtasWXzzzTf4+PgQHh7OX/7yF5o0aULHjh0ZMGAABQUF7Nixgw4dOjB//nzatWtHdnY206ZNY9euXXTu3JkWLVpcdM0ioCMTkSq++OILysrK6N27t6vN4XDQvn17xo4dS7t27dz6OxwOTp8+zfr16wGq3F7jQnz99dd069aN7t27M3DgQMrLy/njH//o1ufgwYPMnj2b2NhYXnvtNcxmMwsWLCAuLo533nmHbdu2ufpmZGTQq1cvRo4cyRdffMGrr74KwJ///Gc+/PBDxo0bx/33388nn3xy0TWLgI5MRGpUWloKQF5eHr169XK1P/DAA8ybNw+A06dP061bN9ey0NBQxo8fD0CjRo2q3HzT6XTi7e1d43PeeOONPPfcc5SVlZGbm8vcuXN59NFHycjIcPV56KGHiI6OBipC4YMPPuD//b//5zqf89O7R1utVh599FGKi4t56aWXyMvLAyrubGu1Whk+fLjr8caNGy/sFyTyEwoTkZ/p0aMH3t7ebN68mZEjR9KiRQvWrFnDmTNnGDFihFvfynMmjRo1omXLlm73VmrRokWVLyMrKiqiefPmNT53kyZN6NGjh+vx559/zrJly9i/f7+rzd/fH6i4MWB8fDxFRUU8+eSTdO3alUmTJrkF2E/v+wW4lnl5eblN13l5edXlVyNSI4WJyM8EBgYycuRI/va3v/HEE08QGxuLw+HgvffeA9xvKe7t7e324v9TYWFhrFixgtmzZ9O7d2/27dvHvn37XN+CWZ0zZ864pqlOnTqF3W6nWbNmtG3b1vWprkaNKmanT58+zZdffkmXLl3w8vLirbfeAup299m77rqLjIwM/vGPf9CiRQu3b3kUuRgKE5FqTJo0ibZt2/LGG2/wzDPP4HQ6ad++PZMmTarzbd8nTpxIcXExGRkZvPbaa/j5+TFw4EAmTZpU4zqVnxiDitAKDg5m0aJFrqORn/L39+eJJ55g5cqVzJgxg4iICMxms9tRTE2mTp3KuXPnSE1NpUOHDgwcOJC1a9fWaVwi1dFdg0VExDB9mktERAxTmIiIiGEKExERMUxhIiIihilMRETEMIWJiIgYpjARERHDFCYiImLY/we2iO8OeKGtVgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['Gpu brand'],y=df['Price'],estimator=np.median, palette=sns.color_palette(\"husl\", 3))\n",
    "plt.xlabel('GPU Brand', fontsize=12,fontweight='bold')\n",
    "plt.ylabel('Price', fontsize=12,fontweight='bold')\n",
    "plt.title('GPU Price distribution', fontsize=14)\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4214,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['Gpu'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4215,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>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>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.83</td>\n",
       "      <td>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.6832            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.5232            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.0000            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.3360            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi      Cpu brand  HDD  SSD Gpu brand  \n",
       "0  226.983005  Intel Core i5    0  128     Intel  \n",
       "1  127.677940  Intel Core i5    0    0     Intel  \n",
       "2  141.211998  Intel Core i5    0  256     Intel  \n",
       "3  220.534624  Intel Core i7    0  512       AMD  \n",
       "4  226.983005  Intel Core i5    0  256     Intel  "
      ]
     },
     "execution_count": 4215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4216,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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": 4216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['OpSys'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4217,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['OpSys'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.title('OpSys Price distribution', fontsize=14)\n",
    "plt.xlabel('Operating System', fontsize=12,fontweight='bold')\n",
    "plt.ylabel('Price', fontsize=12,fontweight='bold')\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "#### function to summarizing OS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4218,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def cat_os(inp):\n",
    "    if inp == 'Windows 10' or inp == 'Windows 7' or inp == 'Windows 10 S':\n",
    "        return 'Windows'\n",
    "    elif inp == 'macOS' or inp == 'Mac OS X':\n",
    "        return 'Mac'\n",
    "    else:\n",
    "        return 'Others/No OS/Linux'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4219,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df['os'] = df['OpSys'].apply(cat_os)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4220,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company</th>\n",
       "      <th>TypeName</th>\n",
       "      <th>Ram</th>\n",
       "      <th>OpSys</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Price</th>\n",
       "      <th>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>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>No OS</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>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>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>macOS</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company   TypeName  Ram  OpSys  Weight        Price  Touchscreen  Ips  \\\n",
       "0   Apple  Ultrabook    8  macOS    1.37   71378.6832            0    1   \n",
       "1   Apple  Ultrabook    8  macOS    1.34   47895.5232            0    0   \n",
       "2      HP   Notebook    8  No OS    1.86   30636.0000            0    0   \n",
       "3   Apple  Ultrabook   16  macOS    1.83  135195.3360            0    1   \n",
       "4   Apple  Ultrabook    8  macOS    1.37   96095.8080            0    1   \n",
       "\n",
       "          ppi      Cpu brand  HDD  SSD Gpu brand                  os  \n",
       "0  226.983005  Intel Core i5    0  128     Intel                 Mac  \n",
       "1  127.677940  Intel Core i5    0    0     Intel                 Mac  \n",
       "2  141.211998  Intel Core i5    0  256     Intel  Others/No OS/Linux  \n",
       "3  220.534624  Intel Core i7    0  512       AMD                 Mac  \n",
       "4  226.983005  Intel Core i5    0  256     Intel                 Mac  "
      ]
     },
     "execution_count": 4220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4221,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df.drop(columns=['OpSys'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4222,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=df['os'],y=df['Price'])\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Operating System', fontsize=12,fontweight='bold')\n",
    "plt.ylabel('Price', fontsize=12,fontweight='bold')\n",
    "plt.title('OpSys Price distribution', fontsize=14)\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4223,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Bsd87d+5k4cKFjBgxosRTqu+nTZs2bN68mQYNGhhfWyjcQnP8+HHy8vKMhaKbmxuPP/44H3zwAfv27SM6OprJkydz/vx5ateuXaTdsr6/7nBxcQHg7NmzD3SGmhD/SwoaUWF4e3uzatUqGjVqxJQpUxg8eDDR0dE8/fTT+Pv78/zzzxMdHY2dnR2zZ8/m5MmT9O/fn40bN/LSSy8Z23nllVfw8fFhxIgRPPvss+h0OkaNGsXp06dLlWPQoEFcu3aNRx99tFSb3YODg/H09CzT1pk75s6di6urK8OHD+e9995jyJAhJe6ymDZtGs8//3yRf+np6UybNo2cnBwee+wxXnnlFRo2bEhoaGiR/g8YMIDVq1czZMgQMjMzWbJkiXF9TzzxBHv37mXcuHHY2dmxePFiNBoNTzzxBK+//jrdu3dn9uzZxrZ8fHzw9/dn2LBhfPfdd3zyySfGLQyzZs2iWrVqPPPMMwwZMgSDwcA//vGP+/Zp69atdO7cmc6dO9O9e3dmzpzJ0KFDmTp1KlDy2Ba3vv79+/Pmm2+yaNEiwsLC2L59O4sXL6ZJkyYAvPfeewQHBzN27FimTZvGU089dd+crq6ufPfdd1y/fp3BgwcTHh7OmDFjjEV3WQQHB5Obm2vcrfT317d69ep3XX9mypQpdOrUiUmTJjFs2DByc3NZsmTJPXeHleX9dYenpydDhgzhzTffNMmVr0XFoVFkR6YQ6PV61q5dS5cuXfDz81M7ThHZ2dl07NiRNWvWUK9evTItHxkZSdeuXY1fMps3b+bTTz8tcsXi8jJ69Ghat27NpEmTHrqttWvX8vnnn7N79+5ySCZMwdzvLyHuRw4KFoLCAyyfeOIJtWPcZcuWLURERNC4ceMyFTMAjo6OTJs2jREjRjBs2DCSkpJYvHgxoaGh5ZxWVETy/hKWQgoaISzY/Pnzyc/Pf6jbMtzZrfPJJ5/www8/GI/nKI8tKELI+0tYCtnlJIQQQgirJwcFCyGEEMLqWX1BoygKubm5cpEmIYQQogKz+oImLy+PkydPPtSFwqxJSXfotQUVoY8g/bQlFaGPIP20JbbYR6svaO4o7UXNrF1OTo7aEUyuIvQRpJ+2pCL0EaSftsQW+2gzBY0QQgghKi4paIQQQghh9aSgEUIIIYTVk4JGCCGEEFZPChohhBBCWD0paIQQQghh9aSgEUIIIYTVk4JGCCGEEFZPChohhBBCWD0paIQQQghh9aSgEUIIIYTVk4JGCCuTn28ZbQghhCXRqh1ACPFgtFqYNevh2njY5YUQwtLIFhohhBBCWD0paIQQQghh9aSgEUIIIYTVk4JGCCGEEFZPChohhBBCWD0paIQQQghh9aSgEUIIIYTVk4JGCCGEEFZPChohhBBCWD0paIQQQghh9aSgEUIIIYTVk4JGCCGEEFZPChohhBBCWD0paIQQQghh9aSgEUIIIYTVk4JGCCGEEFZPa+oVZGRkMGLECL766iuio6OZN2+ecVp8fDwtW7bk66+/ZtGiRfzyyy9UqlQJgOHDhzNq1ChTxxNCCCGEDTBpQXPs2DFmzJjBlStXAOjWrRvdunUDIDExkZEjRzJ16lQATp48ybx58wgKCjJlJCGEEELYIJPuclq1ahUzZ87Ex8fnrmmffPIJI0aMoHbt2kBhQfPtt98ycOBAwsPDyc3NNWU0IYQQQtgQkxY0s2fPpk2bNnc9f+XKFQ4ePMiYMWMAyMzMpEmTJkyePJl169aRlpbGl19+acpoQgghhLAhGkVRFFOvpEePHvz444/UqFEDgI8//hgPDw8mTJhwz/lPnz7NtGnTWL9+fYlt5+bmcvLkyfKMK4RFCw4OZvz42Idq45tv/IiKiiqnREIIYR7BwcH3nWbyg4LvZefOnSxZssT4ODY2lsjISIYNGwaAoihotQ8erbiO2oqoqCib72dF6CM8XD/9/Pweev3meo0rwnhWhD6C9NOW2GIfzX7adnJyMjk5OQQEBBifc3Jy4tNPP+X69esoisLy5cvp3bu3uaMJIYQQwkqZfQtNTEwM1atXL/Kcl5cX4eHhvPjii+j1elq3bs2zzz5r7mhCCCGEsFJmKWgiIiKMPwcGBrJq1aq75gkNDSU0NNQccYQQQghhY+RKwUIIIYSwelLQCCGEEMLqSUEjhBBCCKsnBY0QQgghrJ4UNEIIIYSwelLQCCGEEMLqSUEjhBBCCKsnBY0QZpafX/i/rV12XAgh1KTKvZyEqMi0Wpg1q/AeZmW5J9OsWeUeSQghrJ5soRFCCCGE1ZOCRgghhBBWTwoaIYQQQlg9KWiEEEIIYfWkoBFCCCGE1ZOCRgghhBBWTwoaIYQQQlg9KWiEEEIIYfWkoBFCCCGE1ZOCRgghhBBWTwoaIYQQQlg9KWiEEEIIYfWkoBFCCCGE1ZOCRgghhBBWTwoaIYQQQlg9KWiEEEIIYfWkoBFCCCGE1ZOCRgghhBBWz+QFTUZGBgMGDCAmJgaAqVOn0qdPHwYNGsSgQYPYvn07AJGRkQwcOJA+ffowf/58U8cSQgghhA3RmrLxY8eOMWPGDK5cuWJ87uTJkyxbtgwfHx/jczk5OUybNo2ffvoJX19fJkyYwK5du+jWrZsp4wkhhBDCRph0C82qVauYOXOmsXjJysoiNjaWd999l4EDB7JgwQIKCgo4fvw4tWrVIiAgAK1Wy8CBA9myZYspowkhhBDChph0C83s2bOLPL516xYhISGEh4fj4uLChAkTWLNmDS4uLnh7exvn8/HxIT4+/oHXFxUV9dCZrUFF6Kct9zE4OJjY2FgA4/8Pxq+MyxVtw5yvsS2P5x0VoY8g/bQl1tjH4ODg+04zaUHzvwICAli8eLHx8ejRo1m/fj19+/a9a16NRvPA7RfXUVsRFRVl8/2sCH308yssSvz8/Mq0vH91H5yyUsh2rQpl+KyA+T4vFWE8K0IfQfppS2yxj2YtaM6dO8eVK1cIDQ0FQFEUtFot1apVIykpyThfQkJCkWNshBCFalzew82nNzF8x2Ec8jLJdXTnlk9jTgU9yc2ANmrHE0II1Zj1tG1FUZgzZw63b99Gr9ezcuVKevfuTcuWLbl8+TJXr17FYDCwceNGunbtas5oQlg0bV4WHSI+ovumqeQeO8fV+j34q/NErtV7hEqp1+n96yTa/LkA+/xctaMKIYQqzLqFpnHjxowfP56RI0eSn59Pnz59GDBgAAAfffQRr776Krm5uXTr1u2eu6GEqIicMm/RZ8NEKqVc40TwaAb9NpbvZ//3o2uvz6H1vq9ocnw1VRLOsGPQ5xi0jiomFkII8zNLQRMREWH8edSoUYwaNequeTp06MCvv/5qjjhCWA1tXhY9fn8H1/R4dgyaz80awQzWFZ3HoHPir66vk+AbSNdtM+kQMZc9vWeW+dgaIYSwRnKlYCEslMaQT9et7+GZFM2u0HBu1ij+AL6rDXpwuMML1Lmwk5YHl5gppRBCWAYpaISwUC3/+h7/awc40O0NYmt3KNUyp4Ke5GLj/gQe+hfescdNnFAIISyHFDRCWCCvhHM0O7yc6MZ9udjs0dIvqNHwV9fXyXCrRvvd/0BTkG+6kEIIYUGkoBHCwtgZ9HTcOYccF08OdXrtgZfP1zlzqMtreN66ROPjv5ggoRBCWB4paISwMM2jfsIz+RL7H3mbPCf3MrVxvU4XbtQMoeXBJThnJpW8gBBCWDkpaISwIC7p8TQ7vJzLDXpxo3bHsjek0XCw6+vYG/JoHvVT+QUUQggLJQWNEBak5cElaBSFIyETHrqtjMr+XGrUl/qnN+KUlVwO6YQQwnJJQSOEhfBIuki9s1s4GziUzErVy6XNk61HYVeQT5OjK8ulPSGEsFRS0AhhIVrv+4o8RzdOBo8ptzbTPQK4Vu8RGp1ch0NOerm1K4QQlkYKGiEsgHfcCfyvHeBk8OgyHwh8PyeCR6PTZ9PoxJpybVcIISyJFDRCWIDmh5eR41SZ880Hl3vbqVXrc6NmCA1PrkdjkOvSCCFskxQ0QqjMM+kiNa5EcjZwGPk6Z5Os43zzwbhkJVPjyl6TtC+EEGqTgkYIlTU7vAy9zplzLYaabB03arUn082Hhqc2mGwdQgihJilohFCRe2oMtS7+h3PNHyv3Y2f+TrHTcrHJAPyu/4Xb7ViTrUcIIdQiBY0QKmp8fDWKxo6zLR83+bouNB1AgcaeBqd/Nfm6hBDC3KSgEUIlDvos6p3dzJUGPcl2rWry9WW7eXOjdgfqndmEopeDg4UQtkUKGiFU0uLqH+j02ZwNNP3WmTsuNgnDOTuFrD/+Mts6hRDCHKSgEUINSgHBl7aQUL05yT6NzLba2JrtyXWsRMYv2822TiGEMAcpaIRQgf/V/XhmxnM2cJhZ11tgr+Nq/e5kbv6Tgowss65bCCFMSQoaIVTQ6MRa0p28uFa3m9nXfblhb5SsHDK3yjVphBC2QwoaIcxMfzUWv2sHOVa7B4q91uzrT/BtgbZGNTLWbDP7uoUQwlSkoBHCzNJ++g1Fo+F47R7qBNDY4TakF1n/+QtDUoo6GYQQopxJQSOEGSn6fNL/vYkbtTqQ7lxFtRxuQ3uDwUDGxl2qZRBCiPIkBY0QZpS5ZQ+GxGQuNHtU1RwOTeqiqxdAphQ0QggbIQWNEGaU9uOvaP19iK3ZXtUcGo0G1wHdyN5zBEPybVWzCCFEeZCCRggz0V+/SfYff+E+agCKnb3acXAb+AgYDGRu2aN2FCGEeGgmL2gyMjIYMGAAMTExAKxcuZIBAwYwcOBApk6dSl5eHgCLFi2ie/fuDBo0iEGDBrF8+XJTRxPCrNJXbQHA/Ym+Kicp5BDYEG1NX9ntJISwCSYtaI4dO8bIkSO5cuUKAJcvX2bJkiX8/PPP/PrrrxQUFPDvf/8bgJMnTzJv3jw2bNjAhg0bGDVqlCmjCWFWiqKQsXIrTp2C0NX0VTsO8P+7ncK6krXrEAXpmWrHEUKIh2LSgmbVqlXMnDkTHx8fABwcHJg1axZubm5oNBoaNmxIbGwsUFjQfPvttwwcOJDw8HByc3NNGU0Is8o5eAL95RiL2Tpzh9uAbpCnJ3NbpNpRhBDioZi0oJk9ezZt2rQxPvb396djx44AJCcns3z5cnr27ElmZiZNmjRh8uTJrFu3jrS0NL788ktTRhPCrNJ/3ozGxbnwuBUL4timGfbVqpD5+261owghxEMx/2VKgfj4eMaNG8fQoUNp377wbI9vv/3WOH3s2LFMmzaNSZMmPVC7UVFR5ZrTUlWEftpUH3PzqLx2B/qOLThy7gzBwcHGLZN3/n8wfmVcrmgbd15j59YNyd+5j6j9B0Bnml8JNjWe91ER+gjST1tijX0MDg6+7zSzFzTR0dE8//zzPPXUU4wdOxYo/KUeGRnJsGGFN+pTFAWt9sGjFddRWxEVFWXz/bS1Pqav3UFCVg61XhqFy//3y8+vsCjx8/MrU5tlXe7v7rzGmU/lcHPzPprk2uMSUv6vu62N571UhD6C9NOW2GIfzXradkZGBs899xwTJ040FjMATk5OfPrpp1y/fh1FUVi+fDm9e/c2ZzQhTCZ9xSa0Narh3ClI7Sj35Nw5GI2TA1lys0ohhBUza0GzZs0akpKSWLp0qfH07C+++AIvLy/Cw8N58cUX6du3L4qi8Oyzz5ozmhAmkR+XSPbuKNyH90VjZ5mXfbJzccK5SzCZ2/aiKIracYQQokzMssspIiICgGeeeYZnnnnmnvOEhoYSGhpqjjhCmE366m1QUIDbcMt+b7uEdiJr+z7056/g0KiO2nGEEOKBWeafjELYAEVRSP95M07tWuBQL0DtOMVy7d0BQE7fFkJYLSlohDCR3CNn0F+4ivsIy7r2zL1o/XxwaN6ArK1S0AghrFOpCppLly6ZOocQNif95y1onBxwHdRD7Sil4hrakZy/TsrNKoUQVqlUBU3//v0ZM2YMmzZtQq/XmzqTEFZPyc0jY90OXPt1wb6Sm9pxSsWlT0coKCAr4oDaUYQQ4oGVqqAZP348MTExvPHGG3Tt2pVPP/3UeH8mIcTdMrfupSA1HfcR/dSOUmqOrRpj7+0lp28LIaxSqQqaN954g4iICH788UdCQkJYunQp/fr147nnnuPq1aumziiE1UlfuQX76lVx7tam5JkthMbODpfeHciKOIiiz1c7jhBCPJBSHxS8f/9+1q5dyx9//IGiKDRt2pQDBw7w1ltvmTKfEBYlvxTf8/kJyWTtPID7433Q2NubPlQZ3K8fLn06UpCWQc6B42VaXggh1FKq69D06NGDuLg4XFxceOyxx3jyySepX78+GzZs4N133zV1RiEshlYLs2YVP0+To9toYzDwY1Jfbt9j3pKWN4f79UOb14bhdjp2zIokqnPr+y5vCX0QQoi/K1VB4+zszIwZMxg8eDCurq7G55s3b85zzz1nsnBCWKO6Z7eQ5NOE217Wd4G6fAcXbtYIosaVvUR1fkXtOEIIUWql2uX0+++/4+/vj6urK6mpqfz6668A1KtXj4kTJ5o0oBDWxDPxAl63oolubPnXnrmfG7U6Uul2DJVSrqkdRQghSq1UBc3ChQt56aWXyMnJ4fz587zzzjssWrTI1NmEsDr1zm7GYKfjSoOeakcps5jaHQHwvyoX2RNCWI9SFTQrV65k6NChODg4EBwczFNPPcXKlStNnU0Iq6Ix5FPnwnZi6nQkz6my2nHKLLOSL6metfG/ul/tKEIIUWqlKmiys7Pp2LEjdnZ22Nvb07p1a7KyskydTQir4n9tP07ZqVxqZD3XnrmfG7U74BN7DG2efM6FENahVAcFBwYGGi+ml5+fz+rVqwkMDDR1NmFB8vMLz4xRuw1LVu/sZrKdPblRs73aUR7ajVodaHZkBb4xh7het6vacYQQokSl+nqZMWMGL774Il988QUAtWrVktO1K5jSnK5cEls+1dch5zb+VyI512IIir31V20J1VuQ5+CK/5VIKWiEEFahVL9569Wrx6ZNm4y3O6hTpw72FnrBMCHUUOf8duwL8rnU2Pp3NwEo9lriAtoWHkejKKDRqB1JCCGKVaqCJi8vj+3bt5OcnIyiKERGRqLRaBg9erSp8wlhFeqf2cQt74akVK2vdpRyE1OrA7Wi/8Az6QIp3g3VjiOEEMUqVUHz1ltvsX37dhRFMT4nBY0QhTwTz+OVdIEDXSepHaVcxdYKAaDG1X1S0AghLF6pCpo9e/bQq1cvRowYgU6nM3UmIaxK/TO/Y7B34EqDXmpHKVc5Ll4k+TTG7+p+TrR5Wu04QghRrFIVNDVq1KBPnz506tTJ1HmEsCp2+bnUOb+da3W7kOdUSe045e5GrQ4E/vUDjtmp5Dp7qB1HCCHuq1QFTffu3fnkk0+IiYkx3stJdjkJAQGX9+KYm050kzC1o5jEjVodaPnX9/hdO8jlRn3UjiOEEPdVqoLm66+/BjCetg1S0AgBUO/s72S6+XDT//53prZmt3wake3sif/VfVLQCCEsWqkKmh9//NHUOYSwOi7p8fhd+4sTbcag2NnoZQw0dsTWak+Ny3vRFBhst59CCKtXqlsftGvXjnr16pGcnEz9+vWpVasW7dq1M3U2ISxavXNb0KAQ3bi/2lFM6kbNDjjmplM1/pTaUYQQ4r5KVdBERkbSu3dv3njjDS5dukSfPn3YsmWLqbMJYbmUAuqd2cRN/9ZkVPZTO41JxdZsS4HGHv8r+9SOIoQQ91Wqguazzz6je/fuKIqCwWCgTZs2zJ8/39TZhLBYPrHHcE+L5WIT2946A6B3dCfBt4XcfVsIYdFKVdBcvnyZHj16AODs7MwTTzxBQkKCSYMJYcnqn9lEnoMr1+p2UzuKWdyoFYLXrYu4ZMjnXghhmUpV0NStW5fffvsNgF27drF48WIaNiz5yqEZGRkMGDCAmJgYoHDX1cCBA+nTp0+RLTxnzpxh6NChhIaGMn36dPLz88vSFyHMQpebTq3o/3Clfk8MOie145jFjVodAfCTrTRCCAtVqoJm+vTpHD16FEVRWLx4MdevX2fq1KnFLnPs2DFGjhxpvKFlTk4O06ZN48svv2TTpk2cPHmSXbt2AfD222/z7rvvsnXrVhRFYdWqVQ/XKyFMqN7ZLWjzcznffJDaUczmtldtMtyrU+OqHEcjhLBMpSpoWrduzbZt2/j6669ZvHgxO3fupFWrVsUus2rVKmbOnImPjw8Ax48fp1atWgQEBKDVahk4cCBbtmzhxo0b5OTkGNsbMmSIHHAsLJaiKDQ8tYEknyYV6/5GGg03aoVQ/XoUdoY8tdMIIcRdynQdmhs3bgAwZsyY+y4ze/bsIo8TEhLw9vY2Pvbx8SE+Pv6u5729vYmPjy9NrLtERUWVaTlro0Y/g4ODiY2NfchW/Eqd3VLHsmmOHZVTrvJ76xcf4vXwMy5btjb8ymUsHrSN4+6NaJS/Hu2JCKDvA42RpY5neaoIfQTppy2xxj4GBwffd1qpCpo5c+bc9ZxGoym2oPlff79T99/buN/zZVFcR21FVFSUav3083v405NLk13NPpbk5riZ5Dq6c7vtUPy0jmVux8+vsKAo62taHmPxoG3ovb3IPzifwPTzQN9Sj5Elj2d5qQh9BOmnLbHFPj7QFhpFUcjKyuKnn36ie/fuD7SiatWqkZSUZHyckJCAj4/PXc8nJiYad1MJYUny42+R+fsuopsNxfAQxYy1MuiciPdvjf/VfcBrascRQogiSn2l4Hbt2tG+fXu6d+/OSy+9xPfff/9AK2rZsiWXL1/m6tWrGAwGNm7cSNeuXfH398fR0dG46Wv9+vV07dr1wXsiiiUnjj28tH9tgHxDhToY+H/F1O5Apdsx5EVfVzuKEEIU8cDH0BgMBnbt2kVKSsoDrcjR0ZGPPvqIV199ldzcXLp160bfvn2Bwgv3zZgxg8zMTJo2bfpAu7JE6Wi1MGtW2Zd/mGVtQUFOLmk/rMeldwfSPWqqHUc1hadvzydryx4cXh6pdhwhhDAq8zE0pb3TdkREhPHnDh068Ouvv941T+PGjVmzZk2p2hNCDRlrd2BITKHyC8MhouT5bVVmpercqtoAp01/4iEFjRDCgjzwFhqNRoOXlxf16tUzWShhnbR5WVSLPYJjThoAOU4eJPoFondwVTnZw1EUhdvfrMahaV2cuwRX6IIG4HrdLlT563vyE5LR+nipHUcIIYBSFjRNmza967mMjAzjz25ubuWXSFgd77gTtDj0L6rHHMa+QF9kWoHGnsTqzTjb8nGUgq5o7Ep12JZFydl7hLxT0Xh/PqXMZ+DZkut1u9Lq4FKytuyh0phH1Y4jhBBAKQuaNm3a3PcXuUaj4fTp0+UaSlgHh5zbBEf+k/pnfifLtSrnWgzhRu0OZLj7ggZc027iGxNFrYsRdNvyLjGP1KXKh6/i0rWN2tEfSMrCf2NX1QO3ob3UjmIRUr3qoq3tR+amP6WgEUJYjFIVNI899hibN2+mV69e5OXlERERQfv27eX06grMNS2OXr++gVt6HKeCRnK8zTPkO7gUmSejkh/xNVpzrN1Yal2MoMfFpcQNnUTlcUPxevcF7Fws/z5IucfOkR1xAK/p47Fzqninat+TRoNr/67c/u4XCtIzsXO37l2KQgjbUKqC5uLFi8yYMYNhw4YBsHz5ciIiIpg7d65JwwnLVPnWZXr99gb2+TlsG7yQRN8Wxc6v2NlzpWFvaizpSvIHX3H72zVk7z1C9eUfowuobqbUZZMy/yfsKrlRaexjakexKK79u3D7y5/J3LEP98dky5UQQn2lOqDh4sWLRY6ZKSgo4OjRo6bKJCyYS3o8fTa8BkoB2x5bVGIx83d2zo5UnTMR358/I/9GAjdCx5MTdcqEaR9O3rnLZP6+i8rjhmJfSY4T+zunNs2w9/Yka9OfakcRQgiglFtoQkJC+PTTT9m5cycajYbDhw/TqVMnU2cTFsY+P5dHNk/DzqBn67CvSfOsVaZ2XHq2x3/zP4l78h1iB79GtaUf4tq7QzmnfXgpXyxD4+JE5fHD1I5icTT29rj07UzGup0ouXloHB3UjiSEqOBKtYXmww8/pGfPnpw7d46LFy8SFhYmu5sqGEVRaP/Hp1RJPM/eXu+WuZi5w6FhbWps+Rpdw9rcfHoaGb/vLqek5SPv7GUyftlBpacHYV/FQ+04Fsm1XxeUjCyydlvfDe6EELanVFtoqlSpwoIFC4DCLzY5dbXiyVi9lXrntnK03Vhi6pTP1jn7qp74rf2cuBFvE//ce/DVe7gN7lEubT+sW7O/wc7VGc+JT6kdxWK5dA1G4+ZC5uY/LXILmxCiYinVFprk5GQmTpxI69atOXHiBBMmTODmzZumziYshFNWCkkzFpJQvTkn2jxdrm3bV3bHb/U8nNo2J/6FcDJ++6Nc2y+L7P3HydqyB49Xn5StM8XQODrg2iuErC17UAwGteMIISq4Uu9yOn36NNnZ2aSlpXH06FGmT59u6mzCQrTZs5CCjCz2d38HNOV/YTw7Nxd8V3yCU+smxI+fhXbfiXJfR2kpikJy+D+xr1aFyuMfVy2HtXDp3wVDYgo5f1nuwd1CiIqhVN9Oe/fu5aWXXgKgUqVKTJ8+ncOHD5s0mLAMflcPUOfCdjxfH81trzomW4+dmwu+Kz/DMbAhrrN/IHNbpMnWVZzM9RHk/HUSr3fGYufqrEoGa+LaqwM46MjcLGc7CSHUVaqCxsnJicTERKDwbttHjx6latWqJg0m1KcpyCd470JuewSY5VgSO3dXfFf9A0MdP24+O4OsiAMmX+ffGW6nkzRjAY4tG+E+Ksys67ZWdu6uuHQJJvP3XSiKonYcIUQFVqqC5oknnmDevHkoisLo0aNZsWIFQ4cONXU2obJ6Z7fgkXKVIyETzHZarn1ldzLnvIDD/5/9lLX7kFnWC5A8+xsMSal4/+NtNPb2ZluvtXMd+Aj5V+PIPXZO7ShCiAqsVGc5vfTSS/j5+fHHH3+Qn59Ply5dGD58uKmzCRXZ5+cSeHApidWacr1uV7OuW3F3xW/NPGKHTOTmU1PwXfEpzp2CTLrOnEOnSPthA5XHD8OxZSOTrsvWuIZ1JfHtz8hYtxOnVo3VjiOEqKBKtYWmX79+eHt78/nnn7No0SKeeOIJOXXbxjU6vgbXzEQOd3gBVBhr+yoe+K75HG1NX+KenEz2/uMmW5chLYP4F8PR+nnjNWWcydZjq+w93HHp0Z6M9REoBQVqxxFCVFClKmgMBgPx8fGmziIshDYvi+aH/82Nmu1J8DftlpFic3h74vfL52h9qxI34i1y/jpZ7utQFIXE1z8m/3o81b6ZhZ2bS8kLibu4PdYTQ2wCOQfLf4yEEKI0SrXLyd/fn/fee481a9bg4eGBnZ0dGo2GhQsXmjqfUEH9MxtxzE3jeNtny7Xd/HzQluIdFxwcbPxZW60Kfuu+4MajrxL7xFv4rZmHU+um5ZYpbclaMn/7A6+ZL+LUrvT3pRJFuYZ2QuPsSMa6nTiHBKodRwhRAZWqoNm3bx9AkVO1ZZeTbbIz6Gl6dCU3/VqRVL1Zubat1cKsWSXPFxsbi5+f39+e8cal6xc8setVYh97nWrfzsS1z8NfrTjj990kzViIS+8OeLw04qHbq8js3Fxw6d2RzN/+Q9XZr6IpTeUqhBDlqNhdTs8//zxnz57l7NmzbNq0iePHjxsfnzlzxlwZhRnVPr8d14wETrW2rEv+Z7lXw3/jl+jqB3Bz9DRSv179UKcJZ/3nIPHjZ+EY1Jhq38xCY1f+FwysaNwe64khMYVsubeTEEIFxf4W//PPP0lJSSEvL4+wsDDOnj1rrlxCDUoBzY78m+Qq9Ymt2U7tNHfRVq+K/6+LcAntyK0ZC4h/9l0MybcfuJ30tTu4+fQ0HBrWxnfFp3LcTDlx7d0BOw930ldtVTuKEKICKvWfpXLRLNvnf3U/HilXOdX6SVXObCoNO1dnqv8wG6+ZL5K5bS/Xuz5N+todpTq7piAnl8R35pEw4X0cWzTEd9U/sPdwN0PqikHj6IDbYz3J3LSbgvRMteMIISqYEgsaOVam4mh0Yh1ZLlW4Wq+72lGKpbGzw/OVJ6mx5Wvsq3qSMOF9Yro/S/qqLRhS0++avyAji9SvVnKt7QjSvl9H5ZdH4Ld+AVpvTxXS2zb34X1RsnPJ+PUPtaMIISqYEo/cW7FiBTt27ECj0fCvf/3LeMsDjUbDlClTTB5QmId7agz+1/ZzrO1YFHvrOKDTMbAhNSKWkLEhgpSPlpDw8mywt8exVSPsq3pi5+xI3oWr5J29AgYDTp2CqPbPd3Hu3Frt6DbLMbgpunoBpK/cTCW5fYQQwoxK/ObauvW/+8N///13489S0NiWhifXU2Bnz4VmA9WO8kA0dna4P9YLt0E9yD1yhswte8k5dIr8GwkUZGShq1sDj94dce3TEae2zdWOa/M0Gg3uT/Qlec636K/GoqvlV/JCQghRDootaHbu3GmuHEJF9voc6p39nWt1u5Htap03HdXY2eEU3Ayn4PI91Vw8OLfHQ0me+x3pK7fg9c5YteMIISqIYgsaf3//cl/h6tWrWbZsmfFxTEwMgwYNIjs7m6ioKJydnQF45ZVX6N27d7mvX9ytzoXtOOZmcK7FELWjCBugq1EN525tSP/373i++bTacYQQFYTZD5Z4/PHHefzxxwG4cOECL7/8Mq+88gpPP/00y5Ytw8fHx9yRKrwGp34j1asOCb5yhVdRPiqNeZT4se+StfMAVHFUO44QogJQ9Wpis2bNYtKkSTg5OREbG8u7777LwIEDWbBgAQVykzuz8Lh1iaoJZ7jQdIDFnqotrI9r387Y+3iR9uOvakcRQlQQqhU0kZGR5OTk0K9fP27dukVISAhz5sxh1apVHDp0iDVr1qgVrUKpf2YjBjstlxv2UTuKsCEanRb3J8PI2r4PTWKK2nGEEBWAaufn/vzzzzz7bOHNDwMCAli8eLFx2ujRo1m/fj3Dhw9/oDajoirGJdfL0s/g4GBiY2OLPGdv0FPrzBYuVA/mckoWpGQV04LfXcs/uNK3ce/5/FQf43u9jg/uv69D2doy71jcb/mSxsKuVW3cFQWHrQeIqgDX/FH7vWku0k/bYY19/PvNi/+XKgVNXl4ef/31Fx999BEA586d48qVK4SGhgKFVyXWluHmdsV11FZERUWVuZ9Fb/gINS/+B5e8dG60fvyuaaVZvjwy3MvdN6f8L0sY4/J6HYrrp7kyPIzSjEXsI1tRtuyj2SeT0eis4/pGZfEwn0trIv20HbbYR1V2OZ07d47atWvj4lJ4Dx1FUZgzZw63b99Gr9ezcuVKOcPJDOqf+Z1MNx/iAtqoHUXYqMrjhmKXdJuMjX+oHUUIYeNUKWiuX79O9erVjY8bN27M+PHjGTlyJGFhYTRp0oQBAwaoEa3CcM5Mwvf6X1xq1BfFzl7tOMJGufQKweDvze2vV6sdRQhh41TZBty/f3/69+9f5LlRo0YxatQoNeJUSLXP78BOKeBSo1C1owgbprGzI3dQV+y//IWcQ6dwaiMXPhRCmIaqp20L9dQ9t5UknyakedZUO4qwcXm922FXyY3bX69SO4oQwoZJQVMBedyKxuvWRdk6I8zD2RH30QPI+G0X+ph4tdMIIWyUFDQVUN1zWymws+dKg55qRxEVROVxw0ADtxevUDuKEMJGSUFTwWgKDNQ5v50bNUPIdfZQO46oIHQ1quH+eChpy34jXy60J4QwASloKphqsUdxyUySKwMLs/N4bRRKrp7bX61UO4oQwgZJQVPB1L6wE73OmZjaHdWOIioYh/o1cX20O7eXrsNwO13tOEIIGyMFTQViZ9BTM3oX1+t0xqBzUjuOqIA8Jz6FkpHF7W/lXm1CiPIlBU0FUv36IRxz0+RgYKEaxxYNcO3fhdtfrsSQfFvtOEIIGyIFTQVS+2IEuY5uxAW0UzuKqMA8p4yjICOL1EX/VjuKEMKGSEFTQRTk5BJwaTfX63ajwF6ndpwyyc+3jDbEw3FsUhe3Yb25/d0v5N9MUjuOEMJG2O7tb0URWTsP4KDPsurdTVotzJr1cG087PKifHi9PZaMdTtJmfcvvD95U+04QggbIFtoKoiMdTvJcfbgpn+Q2lGEQFfHn0qjB5L242/knb+idhwhhA2QgqYCKMjIImvbXq7W645iJxvlhGXwfHssdi5O3Jq5WO0oQggbIAVNBZC5PRIlO5crDXqoHUUII623J55vPUPWjv1k7TygdhwhhJWTgqYCyFi3E/vqVUnwDVQ7ihBFVB43FF2dGiS9txBFL0dsCyHKTgoaG2e4nU7WzgO4De4BGhluYVk0DjqqhL+M/vxVbn+zWu04QggrJt9wNi5z05+Qp8ftMes9u0nYNpfQTriEdiL5k6XoY+LVjiOEsFJS0Ni4jHU70dbyxTGoidpRhLgnjUZD1bmvA5A07XNVswghrJcUNDbMcCuV7N1RuA3uiUajUTuOEPelC6iO59vPkrV5D5mbdqsdRwhhhaSgsWEZG3eBwYDbYNndJCyfx4ThODSrT+Lb/8CQkqZ2HCGElZGCxoZlrNuJrkEtHJrVUzuKECXS6LT4LJyGIfk2STMWqB1HCGFlpKCxUfk3k8iJPIrbY7K7SVgPxxYN8Hx9NBmrtpK5da/acYQQVkQKGhuVseE/oCiFp2sLYUU8J40p3PU06WPyE5LVjiOEsBJS0NiojA0RODRvgEODWmpHsShyt23Lp3HQ4fPPdylIzyTh1TkoBQVqRxJCWAG5sY8N0l+LI/evk3jNmKB2FIvzsHfslrt1m4djk7pUCX+FpHfmcfvrVXi8OELtSEIICydbaGxQxoYIANndJKxapWcG49q/C7c++JrcY+fUjiOEsHBS0NigjHU7cQxuiq6Wn9pRhCgzjUaD9+dTsPf2In78LAoysopML4/dh7ILUgjbocoupzFjxnDr1i202sLVh4eHc+3aNf75z3+i1+t55plnGDVqlBrRrF7exWvknbhAlQ9eVTuKEA/N3rMS1b6cQeyQ10ma+jk+C6cZpz3s7kOQXYhC2BKzFzSKonDp0iX++OMPY0ETHx/PpEmTWLt2LQ4ODowYMYL27dtTv359c8ezehnrd4JGg9ug7mpHEaJcOHcKwvONMaR89gPOXYNxfzxU7UhCCAtk9l1Oly5dQqPR8Pzzz/Poo4+ybNkyIiMjCQkJwcPDAxcXF0JDQ9myZYu5o1k9RVHIWLcTpw4t0fp6qx1HiGI9yO4ezzefxqlDSxLf/JTcUxdNF0oIYbXMvoUmLS2NDh06MGvWLHJychgzZgz9+vXD2/u/X8A+Pj4cP378gduOiooqz6gW6379tLscS6XzV7ndpy03/2ee4OBgYmNjH2Ktfg+5/IO1ce/5zJvBNMsXbaNsbVnG6/Agn7d7zRscHMz48aXP4OrxEk8rU0gOm8K/HpnLwh8alMvrUF6/Nyr67x9bUxH6aY19DA4Ovu80sxc0QUFBBAUFAeDi4sKwYcOYO3cuL7zwQpH5ynJ12+I6aiuioqLu289bmw+Ram9Pk5fGoPX2vGu6n9/DHST8sMuXto3Y2Nj7zmeuDKZc/k4bxfXTXBkeRmk/b8W9Zx8sgx97nWfTe/1rDDv1HUrB3HJ5Hcrj90ZxfbQl0k/bYYt9NPsup0OHDrFv3z7jY0VR8Pf3JykpyfhcQkICPj4+5o5m1ZSCAjJ+2Y7LI23vWcwIYQsSfVtwqPOr1LgSScr8H9WOI4SwIGYvaNLT0/nkk0/Izc0lIyODdevW8emnn7Jv3z6Sk5PJzs5m27ZtdO3a1dzRrFrOgRPkx8TjNqy32lGEMKnzzR/jUsM+pHy8FL+r+9WOI4SwEGbf5dS9e3eOHTvG4MGDKSgo4MknnyQ4OJhJkyYxZswY9Ho9w4YNIzAw0NzRrFrGL9vRuDjh2rez2lGEMC2Nhv2PvE1jXTSdt4ez6fFvyajsr3YqIYTKVLkOzeuvv87rr79e5LmBAwcycOBANeJYPSVPT8aGCFz7d8HOzUXtOEKYnEHnRPUf5pDe+Xm6b5rK5qFfke8g730hKjK5UrANyNq5n4LUdNyG9lE7ihBmo6vtx+7Q96mUco3O2z8ARW5iKURFJgWNDUhfsx27qh64PNJG7SiigniQa8iY8kyKmwFtONT5FQKu7KHVgSUmW48QwvLJ3batnCEtg6yte6k0eiAarQynMI8Hue3A/U5PL6/bDpxrMRTPpIu0iPqRlCr1uNpAbsoqREUkW2isXObGXSi5eXJ2k6i4NBoOdnuDhOrN6RgxB8/E82onEkKoQAoaK5exZhu6OjVwbN1U7ShCqKbA3oFd/WaT61SZ7pum4pSVonYkIYSZSUFjxfLjEsnecwS3Yb3LdGVlIWxJjosXf/Sbg2N2Kl23zMDOoFc7khDCjKSgsWIZ63aCouA2VHY3CQGQ7NOIfT2mUi3uOG13fw6KonYkIYSZyFGkVix99TYcWzfBoV6A2lGEsBhXGvbC89ZFmh9eTkrV+pxv8ZjakYQQZiBbaKxU7plL5J28INeeEeIejrZ/npjaHWn75xdUizmsdhwhhBlIQWOl0v/9O+i0uA/tpXYUISyOYmfPnt7vkeZRg65b38PtdqzakYQQJiYFjRVS8vSkr96Ka9/O2FfxUDuOEBZJ7+DKH/3nolEK6L5pCrq8TLUjCSFMSAoaK5S5LZKCW7dxfzJM7ShCWLR0jwB29/2ASqnX6LJ1FpoCg9qRhBAmIgWNFUr/9+/Y+3rj0r2t2lGEsHg3awRzsMsk/K/tJzhysdpxhBAmIgWNldEkpZK18wDuT/RFY2+vdhwhrMKF5oM4EziMJsdW0+DUr2rHEUKYgBQ0VsZh+0EoKMB9RD+1owhhVaI6vcyNmu1pt3uenPkkhA2SgsaKKAYDjpv34dw1WK49I8QDUuy0/NlnFmmVa9BtywzcU6+rHUkIUY6koLEiWTv3Y5eQQqWnB6kdRQirpHd04z9hH6No7Oj++2QMqelqRxJClBMpaKxI2g8bKPCshGu/LmpHEcJqZVT2Z1e/D3FLiyN+3Hso+ny1IwkhyoEUNFYgPx/01+LI2rGfKs8+hkYnd6wQ4mEk+LXiwCNvkb3rEImT56HIPZ+EsHryzWgFtFpYP/43mqFh9ql2uM968DZmlWEZIWxZdJMw+reIIfWLZehq+uL5+mi1IwkhHoIUNFagIDuXBqd+5UatjqS7VMVd7UBC2Aivac+THxNP8uxv0AZUx13uXC+E1ZKCxgpk/LIdp5zbnGn5uNpRhLApGjs7fL6YQn5cIgmvzUVbvSrOnYLUjiWEKAM5hsbCKYpC6terSK5Sj3h/+UUrRHnTODpQ/V9z0NX24+bT08g7f0XtSEKIMpCCxsJl7zqE/uxlzrQcDhqN2nGEsEn2Hu74rvgUjYMDcSPeJj8uUe1IQogHJAWNhbv91Srsvb240rCX2lGEsGm6mr74rvgEQ0oasY+/gSH5ttqRhBAPQAoaC5Z39jJZO/dTaexgCuwd1I4jhM1zbNkI32UfkX8llrgRb1OQkaV2JCFEKalS0CxatIiwsDDCwsL45JNPAJg6dSp9+vRh0KBBDBo0iO3bt6sRzaKkfP4TGhdnKo8donYUISoM505BVFsSTu7x88SNnkpBTq7akYQQpWD2s5wiIyPZs2cP69atQ6PRMG7cOLZv387JkydZtmwZPj4+5o5kkfSXYshYt5PKLwzH3quy2nGEqFBcQzvhs2gaCS9+QPz4WVRf+gEarZwUKoQlM/sWGm9vb6ZMmYKDgwM6nY569eoRGxtLbGws7777LgMHDmTBggUUFBSYO5pFSVm4HI1Oi8eLT6gdRYgKyX1YH6p+NImszXtIeHk2Sr7cIkEIS2b2gqZBgwa0atUKgCtXrrBp0ya6dOlCSEgIc+bMYdWqVRw6dIg1a9aYO5rFyI9NIH3lFtyf7I+2elW14whRYVV+bghe775AxtodxI9/X+77JIQFU20b6oULF5gwYQKTJ0+mbt26LF682Dht9OjRrF+/nuHDhz9Qm1FRUeUdUxXOC1fhUFBATLcWXIuKIjg4mNjYWOP0v/9cen5lXK68ln+wNu49n3kzmGb5om2oM5bl0caDLW+a8Syf16HE3xudGuM4fjB8s55zSUlkTn0GHO7+1Wkrv39KIv20HdbYx+Dg4PtOU6WgiYqK4rXXXmPatGmEhYVx7tw5rly5QmhoKFB4MTltGfZXF9dRa6G/FMO1rQeoNGYQ9cP6GJ/38/MDCr8Y7vz8oMq6XHktX9o2iuujuTKYcvk7bag5luXRRmmXN+V4lsfrUKrfG8HB3K5bh6Qp8/FbsIZq33+InZOjcXLU///hYeukn7bDFvto9l1OcXFxvPzyy3z22WeEhYUBhQXMnDlzuH37Nnq9npUrV9K7d8W8p0ryx0vQOOjwfGOM2lGEEH9T+bkheM97m6ydB7j51BQKMrPVjiSE+Buzb6FZsmQJubm5fPTRR8bnRowYwfjx4xk5ciT5+fn06dOHAQMGmDua6nJPXCBj7Q48Xh8tx84IYYEqjX4UdDoSJ37EjUdfwXf5x/JZFcJCmL2gmTFjBjNmzLjntFGjRpk5jeVQFIVb4f/EzsMdj1dGqh1HCHEflUb0w76KB/HPzySmz3h8l3+sdiQhBHKlYIuRtWUP2X/8heebz2Bf2V3tOEKIYrj27oD/xi9Bo+HGgJfRHjildiQhKjwpaCxAQXYuSe8uRNe4DpWfk6sCC2ENHJvXp8bWr9HVD8D1/e9I/WoViqKoHUuICksKGguQ+uUK8q/G4T33dTQ6uRqpENZCW70q/r8uQh/SnFvvLiT++VkUpGeqHUuICkkKGpXpL8WQ+sUyXB/tjnPn1mrHEUI8IDtXZ7JmPIvXjAlk/vYHMb3GkXP0rNqxhKhwpKBRkVJQQMLEj9DodFT98FW14wghysrODs+JT+G37gsKsnO50e8Fkj/7Xm6XIIQZSUGjotvfrSVn/zGqfPgqWl9vteMIIR6Sc8dWBOz+F26DepDy8VJi+oyXrTVCmIkUNCrRX4oh+cOvcOkZgvuIfmrHEUKUwZ0NMH+/4qq9hzvVvnqPaks/wJCQzI3QCSRN/RxDSlqxbQghHo4cgaqCgpxcbo57D42DDu95b6PRaNSOJIQoA60WZs263+0dHkE3sA1B+7+mwZJ1JPy0neNtn+V880cpsHcwzjVrljkTC2G7ZAuNCm69t5i8ExfwWTQdrZ+P2nGEECaid3TjYLc3+X34EpK9G9B2zxc89tMTND62Cq1ebp0gRHmSLTRmlrFuJ2nfr6PySyNw7dtZ7ThCCDNIrVqfHY/OxzfmEM0P/UjbPQtpcehHzrQcjiFtCPaV3NSOKITVk4LGjHIOnyZh4lyc2janyowJascRQpiTRkNcQFviAtriHXucFlE/EnTgW662WIbboO5UemoAjm2bP/Au6Pz8wl1fQlR08jEwE/21OG4+NQV7Hy+q/WuOXEBPiAos0S+QCL/P8Eo8xwjdOpJXRpC+YhO3PWtxsUkYlxv0ItutdGc+yjE4QhSSb1UzMCSlEDfybZQ8PX7rF6D19lQ7khDCAiR7N8Jn1hS+cXmNWhf/Q/0zGwmO/JLgyC9JrNaU63W7cq1uF9I9aqodVQiLJwWNiRmSUogd+jr51+Lw/fkzHBrWVjuSEMLC5Du4EN00jOimYVRKuUbNS7sIuLSb1vu+ovW+r0j1rE1szXbE1WhDgl9L8h1c1I4sLFRpd0H+/VIDZW3D0lhhZOthSEohdtgk9JdiqL7sY5w7BakdSQhh4dI8a3IyeDQng0fjkh5PwOU/Cbi8l0Yn19P02CoMdlqSqjcrPB6nRhuU/EZorPHbR5jEnUsJlOTelxooZK27MeVTYCJ50deIG/E2hptJVP/pI1y6tVE7khDif1j6X6JZ7tU4FziMc4HDsM/PxTvuBL4xh/C9/hctDyyh1YHvuLLNFad2LXAKCcSpQ0ucWjVG4+hQcuNC2BgL/ihbr+y9R7j57Aywt8Nv3QKc2jRTO5IQ4h5K+9fs/ZjzL1mD1pGbAW24GdCGIx1ewDE7leoxhxnge5js/cfImr0fAI2TA46tm+Ic0rKwwGnTDDs32UVlLe5XZBdk5WCIv0V+wi0MiSko2TkoeYWXmdY46tA4O2HvVRn7qh7Y5VenQOto5uTqk4KmHCkFBaQuWE7yR0vQ1fbDd8Wn6Or4W/xfgUII65Pr7MHVBj3wnNEDby0YbqWSc+A42fuPk7PvGClfLIN5/wJ7exxbNMCpQ0ucQwJxatcC+6pFT0yQ31EWJCmJr6dF43nrEh63Cv93u30DB31WqZt4Eg2Z7tW57RlAmkdNbnvVIbF6c1K9aoPGdq+nK2/hcqKPiSdx0sdk//EXboN74D3vHezcXQHr+itQCGFd/vv7xQPoCnZdoRNo22bhffMkPnHH8Yk9hvc367D/50oA0irXIKlaUxKrNyexejNe+6Iu8nVgfoqioL8UQ86+Y2TvO0bO/mPkX4uj1/9Pz3KtSkqVesT7tSTbpQrZrlXIdvEix6UKep0zBXZaNIqCXUEeWn0OjjlpjOqXzPalN6iUeo1KqdfwiT2BLr/wqtS5jm6FY+4bCI4BaKpXQ7GzV+8FKGfyDn5IisFA2vfrufXh16AoVP3sLSqNeVTuzySEUFW+gwtxNdsRV7MdAHaGPKrEn8X75gm8409TPeYQdc9vA+DyRiccWzXGqU0znNo2x7F1U7Q+XmrGt0mKoqC/cJXsPUfI2XeU7H3HMMTfAsCuSmWcQ1pR+flhrIhqQEqVuuQ5VX7gdbgPg+Mni6wUt7RYfOJO4BN3HO+4E9S4up8gIOdAZWJrtiO2ZgixNduR6+xRLv1UixQ0ZaQoClk7D5Ac/k/yzlzCuXs7vD97C11NX7WjCSHEXQrsHUj0CyTRL7DwCUXBNf0m3jdPMaDWSXIOnSL1y58h3wCAfbUqODZvgEOLBoX/N68PBQUmy/ewu70sdbeZ/loc2X8eJvvPKLL/jMKQkAyAva83zp1bF+4K7NASXYNaxj+E42eVYwCNhozK/mRU9udS474AOGan4nhiB83TzuF/bT91z29HQUOib3Ou1+5E3oXOODSoVY4hzMMCh9+yKYpCdsRBUr74iZx9x9DW9qfad+G4PvqIbJURQlgPjYbMSr5kVvKl6qzCnRwFWTnkHj9P7pHT5J6MJu/UBbJ2/WUscio7OxLTrD66ugE41K2B7s6/egEPfeCxreyaz4+/RfbeI8YCJv9qHAD23l44d2mNc+fCf9rafqp9Z+Q6e3C5RkfS/IaBUkCVhHP4X91HjSt7Cd73Fdc7foWuXgAufTvhGtoZp3bN0dhb/q4pKWhKcKfqL8jIIv2X7aR9v468U9HY+/lQdc5EKj09CI2DTu2YQgjx0OxcnHAOCcQ5JND4nJKbR965K+SeuEBMxF6ckzPJ3nOYjFVbiixr7+OFrpYf9r7eaH2rYl+9Klpfb7TV///nalXQuDrb1B9+Sn4+eWcuk3PoJDmHTpMTdYr86OsA2FV2w7lTEB4ThuPcJRhdo9qW2XeNHbeqNeFWtSYcbzcWl/R4JjSLJHPLHm5/s4bbi3/Gzqsyrr074NK3My6PtLXYs+akoCmGYjCQt+cwEe9uo2b0LnT6bJKr1ONs9ylcbtSHghs6mFNyO5byl4MQQtxLcbtrNI4OOAY2xDGwIU1HhRmfL8jKQX/lBvro6+RejMFwJQb9tTjyTkeTtXM/Smb23Y1p7bH3rISdRyXsPdyxu/OzZ+HPjY67k+foit7BDb2DK3oHF/Ic3NA7uKB3cKXAXp3r6ygFBRjib5F3/ip5Z6LJO32JvLOXyTt3GSUrBwB7b08c2zTjgM8AbtYIJqVq/cIDbm8AP5d+XWp/X2S5V8N1zGNUHvsYBemZZEUcJHPrHjK37iV95RY0jg44d2mNS9/OuIZ2Qlu96l1tqLX7TwqaYqT961eSJs8jwMGVq/V7cKHpAJKqNQNLrLKFEKKMynZ1WSegHlDvnssWZGSRH5dI/s0kDHGJGBJTMKSmU5CahiE5jYLUNPLjEik4cwlDShpKRhbtSli/wd6hsMjRuaJ3dP3/oseVPAdXErNdsavkip27K3buLthVciv839Wl8GbAWvvCKyrrtKAooM9HMf7TU3A7ozBbajpOZy+Q8K9t5MfcRH/9Jvk3EkCfb8xh7+2JQ5O6VHpqII7BTXEKboq2pi8ajYbTpXgdLd1/3w+uQHfw6Y5mZD4+cSeocXkPAQf34L7jM5Le+owkn8bEBbTlpn9rEn1bYNA6qlaUSUFTDNeBj6Cr489nO1tWyIsUCSFEWdm5ueDQoFapDy5V9Pl8PD0dXV4mOn0mutxMdHmZOOQV/v/3f/99LgvXtDg887LIWJdJQVomGAwPnd1Ra09WVU+0AdVxCmqCdmB3tAHV0NWviUPjuhXyBsOKnZZ4/yDi/YOI6vQKlVOuEHB5D/5XIml25N+0iPoJg52ORN/mpAc+ivuQXiU3Ws6koCmG1tsTbfd2FOxSO4kQQliu8tjFoNFpyXHxJMelbMXCrFmFJ20o2bkUpGVQkJFFQXomBRlZhVtj8g2Qn4+iN4CmcH3odGh0WjQ6LXaV3Qp3g3lU4siZUwS3kdvV3JdGw22vOtz2qsPJ4NFo87LwiTtO9ZgofGOiyNy4Swqa3377jX/+85/o9XqeeeYZRo0apXYkIYQQJXjYM5SgfI4d0Wg0aFycsHNxKtPy+flgp0WKmQeU7+BCbK0QYmuFAOodB2QxBU18fDzz589n7dq1ODg4MGLECNq3b0/9+vXVjiaEEKICuFOYFXcn6uKofUBvRWcxBU1kZCQhISF4eHgAEBoaypYtW3jllVeKXU5RFOPPubm5Jsnm7Pxwy+fmPlwbf1/ezS0fZ+cH72d5ZjB1G/frozkzmGr5v7eh1liWRxsPsrypxtOSXoeyjmV5ZngYtvLZLA8V5bNZXB8tZSzux8HB4Z6nwGuUv1cEKvr666/Jyspi0qRJAKxevZrjx4/zwQcfFLtceno658+fN0dEIYQQQqisefPmODrefaKOxWyhuVddVZqLELm6utKwYUN0Op1lXrRICCGEEOXGweHe1yOymIKmWrVqHDp0yPg4ISEBHx+fEpezs7PD3d3dlNGEEEIIYeHs1A5wR8eOHdm3bx/JyclkZ2ezbds2unbtqnYsIYQQQlgBi9pCM2nSJMaMGYNer2fYsGEEBgaWvKAQQgghKjyLOShYCCGEEKKsLGaXkxBCCCFEWUlBI4QQQgirJwWNEEIIIayeFDRCCCGEsHpWV9D89ttv9O/fn969e7N8+fK7pi9atIju3bszaNAgBg0adM95rEFGRgYDBgwgJibmrmlnzpxh6NChhIaGMn36dPLz81VIWD6K66etjOWiRYsICwsjLCyMTz755K7ptjCeJfXRVsbyiy++oH///oSFhfH999/fNd0WxhJK7qetjCfAxx9/zJQpU+56PjY2llGjRtG3b19efPFFMjMzVUhXfu7Xz/Xr19O5c2fjWM6fP1+FdOVEsSI3b95UunfvrqSkpCiZmZnKwIEDlQsXLhSZZ8KECcrhw4dVSlg+jh49qgwYMEBp1qyZcv369bumh4WFKUeOHFEURVGmTp2qLF++3MwJy0dJ/bSFsdy7d6/yxBNPKLm5uUpeXp4yZswYZdu2bUXmsfbxLE0fbWEsDxw4oIwYMULR6/VKdna20r17dyU6OrrIPNY+lopSun7awngqiqJERkYq7du3VyZPnnzXtPHjxysbN25UFEVRFi1apHzyySfmjlduiutneHi48ttvv6mQqvxZ1Raav9/A0sXFxXgDy787efIk3377LQMHDiQ8PNxkN6w0pVWrVjFz5sx7Xin5xo0b5OTk0KpVKwCGDBly12tgLYrrJ9jGWHp7ezNlyhQcHBzQ6XTUq1eP2NhY43RbGM+S+gi2MZbt2rXjxx9/RKvVcuvWLQwGAy4uLsbptjCWUHI/wTbGMzU1lfnz5/PCCy/cNU2v1/PXX38RGhoKWO9YQvH9BDhx4gTr16/n0Ucf5a233uL27dtmTlh+rKqgSUhIwNvb2/jYx8eH+Ph44+PMzEyaNGnC5MmTWbduHWlpaXz55ZdqRH0os2fPpk2bNvec9r+vgbe3d5HXwJoU109bGcsGDRoYv+CuXLnCpk2b6Natm3G6LYxnSX20lbEE0Ol0LFiwgLCwMDp06EC1atWM02xhLO8orp+2Mp7vvfcekyZNolKlSndNS0lJwc3NDa228Nqz1jyWxfUTCvv26quvsmHDBnx9fQkPDzdzwvJjVQWNUsINLF1dXfn222+pVasWWq2WsWPHsmvXLnNGNLmSXgNbYWtjeeHCBcaOHcvkyZOpXbu28XlbGs/79dHWxvK1115j3759xMXFsWrVKuPztjSWcP9+2sJ4rl69Gl9fXzp06HDP6bYyliX1E2Dx4sW0bNkSjUbDuHHj2L17txkTli+rKmiqVatGUlKS8fH/3sAyNjaWNWvWGB8rimKssG3F/74GiYmJpbqJp7WxpbGMiorimWee4c033+Sxxx4rMs1WxrO4PtrKWEZHR3PmzBkAnJ2d6dOnD+fOnTNOt5WxLKmftjCemzZtYu/evQwaNIgFCxYQERHBnDlzjNO9vLzIyMjAYDAA1juWJfUzPT2dH374wfjYGsfy76yqoCnpBpZOTk58+umnXL9+HUVRWL58Ob1791Yxcfnz9/fH0dGRqKgooPAIdVu8iaetjGVcXBwvv/wyn332GWFhYXdNt4XxLKmPtjKWMTExzJgxg7y8PPLy8ti5cyfBwcHG6bYwllByP21hPL///ns2btzIhg0beO211+jRowfTpk0zTtfpdLRp04ZNmzYB1juWJfXTxcWF7777jmPHjgGwbNkyqxvLv7OqgubvN7AcPHgwAwYMIDAwkOeff54TJ07g5eVFeHg4L774In379kVRFJ599lm1Y5eLO30E+Oyzz5g7dy79+vUjOzubMWPGqJyu/NjaWC5ZsoTc3Fw++ugj42m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      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.set(style=\"whitegrid\")\n",
    "ax = sns.histplot(data=df, x='Weight', kde=True, color='blue')\n",
    "ax.lines[0].set_color('crimson')\n",
    "plt.xlabel('Weight', fontsize=12, fontweight='bold')\n",
    "plt.ylabel('Frequency', fontsize=12, fontweight='bold')\n",
    "plt.title('Quantifying Laptops Based on Weight', fontsize=14)\n",
    "\n",
    "sns.despine()\n",
    "plt.tight_layout()\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4224,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Weight=%{x}<br>Price=%{y}<extra></extra>",
         "legendgroup": "",
         "marker": {
          "color": "blue",
          "opacity": 0.7,
          "size": 9,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "",
         "showlegend": false,
         "type": "scattergl",
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       "                            \n",
       "var gd = document.getElementById('9058f933-2e37-4e47-8816-a2e8c2eab5e3');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "fig = px.scatter(df, x='Weight', y='Price', title='Scatter Plot of Weight vs Price')\n",
    "fig.update_traces(marker=dict(size=9, opacity=0.7, color='blue', symbol='circle'))\n",
    "fig.update_layout(\n",
    "    width=800,\n",
    "    height=500,\n",
    "    xaxis_title='Weight',\n",
    "    yaxis_title='Price',\n",
    "    showlegend=False,\n",
    ")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4225,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ram            0.742905\n",
       "Weight         0.209867\n",
       "Price          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, dtype: float64"
      ]
     },
     "execution_count": 4225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['Price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Visualizing correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4226,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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       "data": [
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         "xaxis": "x",
         "y": [
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       "                            \n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "correlation_matrix = df.corr()\n",
    "fig = px.imshow(correlation_matrix,\n",
    "                labels=dict(color=\"Correlation\"),\n",
    "                x=correlation_matrix.columns,\n",
    "                y=correlation_matrix.index,\n",
    "                color_continuous_scale='Viridis')\n",
    "\n",
    "fig.update_layout(title=\"Correlation Heatmap\",\n",
    "                  xaxis_title=\"Features\",\n",
    "                  yaxis_title=\"Features\")\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4227,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Price')"
      ]
     },
     "execution_count": 4227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=sns.histplot(data=df, x='Price', kde=True,  color='blue', edgecolor='white')\n",
    "ax.lines[0].set_color('crimson')\n",
    "plt.title('Distribution of Price',fontsize=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4228,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.histplot(np.log(df['Price']), kde=True,color='blue')\n",
    "ax.lines[0].set_color('crimson')\n",
    "plt.xlabel('Log Price')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Distribution of Log Price',fontsize=14)\n",
    "plt.show(\"png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4229,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "X = df.drop(columns=['Price'])\n",
    "y = np.log(df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4230,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Mac</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>Mac</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>Mac</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>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>1.80</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157.350512</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>16</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>276.053530</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</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>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1302 rows × 12 columns</p>\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",
       "1298  Lenovo  2 in 1 Convertible    4    1.80            1    1  157.350512   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30            1    1  276.053530   \n",
       "1300  Lenovo            Notebook    2    1.50            0    0  111.935204   \n",
       "1301      HP            Notebook    6    2.19            0    0  100.454670   \n",
       "1302    Asus            Notebook    4    2.20            0    0  100.454670   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
       "0             Intel Core i5     0  128     Intel                 Mac  \n",
       "1             Intel Core i5     0    0     Intel                 Mac  \n",
       "2             Intel Core i5     0  256     Intel  Others/No OS/Linux  \n",
       "3             Intel Core i7     0  512       AMD                 Mac  \n",
       "4             Intel Core i5     0  256     Intel                 Mac  \n",
       "...                     ...   ...  ...       ...                 ...  \n",
       "1298          Intel Core i7     0  128     Intel             Windows  \n",
       "1299          Intel Core i7     0  512     Intel             Windows  \n",
       "1300  Other Intel Processor     0    0     Intel             Windows  \n",
       "1301          Intel Core i7  1000    0       AMD             Windows  \n",
       "1302  Other Intel Processor   500    0     Intel             Windows  \n",
       "\n",
       "[1302 rows x 12 columns]"
      ]
     },
     "execution_count": 4230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4231,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       11.175755\n",
       "1       10.776777\n",
       "2       10.329931\n",
       "3       11.814476\n",
       "4       11.473101\n",
       "          ...    \n",
       "1298    10.433899\n",
       "1299    11.288115\n",
       "1300     9.409283\n",
       "1301    10.614129\n",
       "1302     9.886358\n",
       "Name: Price, Length: 1302, dtype: float64"
      ]
     },
     "execution_count": 4231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4232,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "df.to_csv(\"clean.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Machine learning modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4233,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.4,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4234,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>205</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>2.40</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>512</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.632118</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.63</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.415547</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>781 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company   TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "205   Lenovo     Gaming   16    2.40            0    0  141.211998   \n",
       "288   Lenovo     Gaming   16    2.50            0    1  141.211998   \n",
       "56        HP   Notebook    4    1.91            0    0  141.211998   \n",
       "384     Asus  Ultrabook   16    1.10            1    0  165.632118   \n",
       "1078  Lenovo   Notebook    4    2.10            0    0  100.454670   \n",
       "...      ...        ...  ...     ...          ...  ...         ...   \n",
       "466     Acer   Notebook    4    2.20            0    0  100.454670   \n",
       "299     Asus  Ultrabook   16    1.63            0    0  141.211998   \n",
       "493     Acer   Notebook    8    2.20            0    0  100.454670   \n",
       "527   Lenovo   Notebook    8    2.20            0    0  100.454670   \n",
       "1193   Apple  Ultrabook    8    0.92            0    1  226.415547   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
       "205           Intel Core i7     0  512    Nvidia  Others/No OS/Linux  \n",
       "288           Intel Core i7  1000  256    Nvidia             Windows  \n",
       "56            Intel Core i3     0  128     Intel             Windows  \n",
       "384           Intel Core i7     0  512     Intel             Windows  \n",
       "1078          Intel Core i5   500    0     Intel             Windows  \n",
       "...                     ...   ...  ...       ...                 ...  \n",
       "466           Intel Core i3   500    0    Nvidia             Windows  \n",
       "299           Intel Core i7     0  512    Nvidia             Windows  \n",
       "493           AMD Processor  1000    0       AMD             Windows  \n",
       "527           Intel Core i3  2000    0    Nvidia  Others/No OS/Linux  \n",
       "1193  Other Intel Processor     0    0     Intel                 Mac  \n",
       "\n",
       "[781 rows x 12 columns]"
      ]
     },
     "execution_count": 4234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4235,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import r2_score,mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4236,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression,Ridge,Lasso\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor,GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4237,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# df['Cpu Name'] = df['Cpu'].apply(lambda x:\" \".join(x.split()[0:3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "    # Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4238,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.7943535393960404\n",
      "MAE 0.2108230443733877\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",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4238,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Ridge Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4239,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.7935724816546795\n",
      "MAE 0.21263050756445856\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",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4240,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.7922444639693013\n",
      "MAE 0.213018386660384\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",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4241,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.7160015436431111\n",
      "MAE 0.22293258534783073\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",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4242,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.7977197154026112\n",
      "MAE 0.20446297319683898\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",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4243,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8576537798053985\n",
      "MAE 0.1732490343100001\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",
    "                              random_state=3,\n",
    "                              max_samples=0.5,\n",
    "                              max_features=0.75,\n",
    "                              max_depth=15)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Gradient Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4244,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score 0.8666188842224086\n",
      "MAE 0.16508409323908657\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=500)\n",
    "\n",
    "pipe = Pipeline([\n",
    "    ('step1',step1),\n",
    "    ('step2',step2)\n",
    "])\n",
    "\n",
    "pipe.fit(X_train,y_train)\n",
    "\n",
    "y_pred = pipe.predict(X_test)\n",
    "\n",
    "print('R2 score',r2_score(y_test,y_pred))\n",
    "print('MAE',mean_absolute_error(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Exporting the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4245,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "# Save DataFrame to a pickle file\n",
    "with open('df.pkl', 'wb') as df_file:\n",
    "    pickle.dump(df, df_file)\n",
    "# Save pipeline to a pickle file\n",
    "with open('pipe.pkl', 'wb') as pipe_file:\n",
    "    pickle.dump(pipe, pipe_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4246,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>Price</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>71378.6832</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.34</td>\n",
       "      <td>47895.5232</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>127.677940</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.86</td>\n",
       "      <td>30636.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>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>135195.3360</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>220.534624</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apple</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>8</td>\n",
       "      <td>1.37</td>\n",
       "      <td>96095.8080</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.983005</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>0</td>\n",
       "      <td>256</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>4</td>\n",
       "      <td>1.80</td>\n",
       "      <td>33992.6400</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>157.350512</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>2 in 1 Convertible</td>\n",
       "      <td>16</td>\n",
       "      <td>1.30</td>\n",
       "      <td>79866.7200</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>276.053530</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>2</td>\n",
       "      <td>1.50</td>\n",
       "      <td>12201.1200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111.935204</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>6</td>\n",
       "      <td>2.19</td>\n",
       "      <td>40705.9200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>19660.3200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Other Intel Processor</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1302 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Company            TypeName  Ram  Weight        Price  Touchscreen  Ips  \\\n",
       "0      Apple           Ultrabook    8    1.37   71378.6832            0    1   \n",
       "1      Apple           Ultrabook    8    1.34   47895.5232            0    0   \n",
       "2         HP            Notebook    8    1.86   30636.0000            0    0   \n",
       "3      Apple           Ultrabook   16    1.83  135195.3360            0    1   \n",
       "4      Apple           Ultrabook    8    1.37   96095.8080            0    1   \n",
       "...      ...                 ...  ...     ...          ...          ...  ...   \n",
       "1298  Lenovo  2 in 1 Convertible    4    1.80   33992.6400            1    1   \n",
       "1299  Lenovo  2 in 1 Convertible   16    1.30   79866.7200            1    1   \n",
       "1300  Lenovo            Notebook    2    1.50   12201.1200            0    0   \n",
       "1301      HP            Notebook    6    2.19   40705.9200            0    0   \n",
       "1302    Asus            Notebook    4    2.20   19660.3200            0    0   \n",
       "\n",
       "             ppi              Cpu brand   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   \n",
       "...          ...                    ...   ...  ...       ...   \n",
       "1298  157.350512          Intel Core i7     0  128     Intel   \n",
       "1299  276.053530          Intel Core i7     0  512     Intel   \n",
       "1300  111.935204  Other Intel Processor     0    0     Intel   \n",
       "1301  100.454670          Intel Core i7  1000    0       AMD   \n",
       "1302  100.454670  Other Intel Processor   500    0     Intel   \n",
       "\n",
       "                      os  \n",
       "0                    Mac  \n",
       "1                    Mac  \n",
       "2     Others/No OS/Linux  \n",
       "3                    Mac  \n",
       "4                    Mac  \n",
       "...                  ...  \n",
       "1298             Windows  \n",
       "1299             Windows  \n",
       "1300             Windows  \n",
       "1301             Windows  \n",
       "1302             Windows  \n",
       "\n",
       "[1302 rows x 13 columns]"
      ]
     },
     "execution_count": 4246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4247,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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>205</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>2.40</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>512</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Gaming</td>\n",
       "      <td>16</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>1000</td>\n",
       "      <td>256</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>HP</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>1.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>141.211998</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>Asus</td>\n",
       "      <td>Ultrabook</td>\n",
       "      <td>16</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.632118</td>\n",
       "      <td>Intel Core i7</td>\n",
       "      <td>0</td>\n",
       "      <td>512</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>Lenovo</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i5</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>4</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
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       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>Asus</td>\n",
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       "      <td>16</td>\n",
       "      <td>1.63</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>512</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Acer</td>\n",
       "      <td>Notebook</td>\n",
       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>AMD Processor</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>AMD</td>\n",
       "      <td>Windows</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>Lenovo</td>\n",
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       "      <td>8</td>\n",
       "      <td>2.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.454670</td>\n",
       "      <td>Intel Core i3</td>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>Nvidia</td>\n",
       "      <td>Others/No OS/Linux</td>\n",
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       "    <tr>\n",
       "      <th>1193</th>\n",
       "      <td>Apple</td>\n",
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       "      <td>8</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>226.415547</td>\n",
       "      <td>Other Intel Processor</td>\n",
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       "      <td>0</td>\n",
       "      <td>Intel</td>\n",
       "      <td>Mac</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>781 rows × 12 columns</p>\n",
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       "     Company   TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "205   Lenovo     Gaming   16    2.40            0    0  141.211998   \n",
       "288   Lenovo     Gaming   16    2.50            0    1  141.211998   \n",
       "56        HP   Notebook    4    1.91            0    0  141.211998   \n",
       "384     Asus  Ultrabook   16    1.10            1    0  165.632118   \n",
       "1078  Lenovo   Notebook    4    2.10            0    0  100.454670   \n",
       "...      ...        ...  ...     ...          ...  ...         ...   \n",
       "466     Acer   Notebook    4    2.20            0    0  100.454670   \n",
       "299     Asus  Ultrabook   16    1.63            0    0  141.211998   \n",
       "493     Acer   Notebook    8    2.20            0    0  100.454670   \n",
       "527   Lenovo   Notebook    8    2.20            0    0  100.454670   \n",
       "1193   Apple  Ultrabook    8    0.92            0    1  226.415547   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
       "205           Intel Core i7     0  512    Nvidia  Others/No OS/Linux  \n",
       "288           Intel Core i7  1000  256    Nvidia             Windows  \n",
       "56            Intel Core i3     0  128     Intel             Windows  \n",
       "384           Intel Core i7     0  512     Intel             Windows  \n",
       "1078          Intel Core i5   500    0     Intel             Windows  \n",
       "...                     ...   ...  ...       ...                 ...  \n",
       "466           Intel Core i3   500    0    Nvidia             Windows  \n",
       "299           Intel Core i7     0  512    Nvidia             Windows  \n",
       "493           AMD Processor  1000    0       AMD             Windows  \n",
       "527           Intel Core i3  2000    0    Nvidia  Others/No OS/Linux  \n",
       "1193  Other Intel Processor     0    0     Intel                 Mac  \n",
       "\n",
       "[781 rows x 12 columns]"
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       "1078    10.267130\n",
       "          ...    \n",
       "466     10.126164\n",
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       "Name: Price, Length: 781, dtype: float64"
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       "      <th>513</th>\n",
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       "      <td>2.80</td>\n",
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       "     Company            TypeName  Ram  Weight  Touchscreen  Ips         ppi  \\\n",
       "248       HP            Notebook    4    1.49            0    1  165.632118   \n",
       "555     Asus            Notebook    4    2.00            0    0  100.454670   \n",
       "1251      HP            Notebook    4    1.86            0    0  100.454670   \n",
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       "885       HP  2 in 1 Convertible    4    1.28            1    0  165.632118   \n",
       "...      ...                 ...  ...     ...          ...  ...         ...   \n",
       "513     Dell            Notebook   16    2.20            0    0  141.211998   \n",
       "312      MSI              Gaming   16    2.80            0    1  141.211998   \n",
       "440   Lenovo            Notebook    8    2.80            0    0  106.113062   \n",
       "160     Asus            Notebook    4    2.03            0    0  100.454670   \n",
       "376   Lenovo             Netbook    4    1.59            0    1  135.094211   \n",
       "\n",
       "                  Cpu brand   HDD  SSD Gpu brand                  os  \n",
       "248           Intel Core i5   500    0     Intel             Windows  \n",
       "555   Other Intel Processor   500    0     Intel  Others/No OS/Linux  \n",
       "1251          AMD Processor   500    0       AMD             Windows  \n",
       "547           Intel Core i5  1000    0     Intel  Others/No OS/Linux  \n",
       "885           Intel Core i5     0  256     Intel             Windows  \n",
       "...                     ...   ...  ...       ...                 ...  \n",
       "513           Intel Core i7  2000  256       AMD             Windows  \n",
       "312           Intel Core i7  1000  256    Nvidia             Windows  \n",
       "440           AMD Processor  1000    0       AMD             Windows  \n",
       "160           AMD Processor  1000    0       AMD             Windows  \n",
       "376   Other Intel Processor     0  128     Intel             Windows  \n",
       "\n",
       "[521 rows x 12 columns]"
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