Laptop_Price_Prediction.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jg6PMSUshirY"
      },
      "source": [
        "## **Laptop Price Prediction**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "wzJF7V7Ohirc"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
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        "id": "UyyNoasLhire",
        "outputId": "257d3f7b-4feb-4fd3-83d7-6339ab09c8d6"
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            "text/plain": [
              "      brand processor_brand processor_name processor_gnrtn ram_gb ram_type  \\\n",
              "0      ASUS           Intel        Core i3            10th   4 GB     DDR4   \n",
              "1    Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
              "2    Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
              "3      ASUS           Intel        Core i5            10th   8 GB     DDR4   \n",
              "4      ASUS           Intel   Celeron Dual   Not Available   4 GB     DDR4   \n",
              "..      ...             ...            ...             ...    ...      ...   \n",
              "818    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "819    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "820    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "821    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "822  Lenovo             AMD        Ryzen 5            10th   8 GB     DDR4   \n",
              "\n",
              "         ssd      hdd       os  os_bit graphic_card_gb      weight  \\\n",
              "0       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "1       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "2       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "3     512 GB     0 GB  Windows  32-bit            2 GB      Casual   \n",
              "4       0 GB   512 GB  Windows  64-bit            0 GB      Casual   \n",
              "..       ...      ...      ...     ...             ...         ...   \n",
              "818  1024 GB     0 GB  Windows  64-bit            0 GB      Casual   \n",
              "819  1024 GB     0 GB  Windows  64-bit            0 GB      Casual   \n",
              "820  1024 GB     0 GB  Windows  64-bit            4 GB      Casual   \n",
              "821  1024 GB     0 GB  Windows  64-bit            4 GB      Casual   \n",
              "822   512 GB     0 GB      DOS  64-bit            0 GB  ThinNlight   \n",
              "\n",
              "        warranty Touchscreen msoffice   Price   rating  Number of Ratings  \\\n",
              "0    No warranty          No       No   34649  2 stars                  3   \n",
              "1    No warranty          No       No   38999  3 stars                 65   \n",
              "2    No warranty          No       No   39999  3 stars                  8   \n",
              "3    No warranty          No       No   69990  3 stars                  0   \n",
              "4    No warranty          No       No   26990  3 stars                  0   \n",
              "..           ...         ...      ...     ...      ...                ...   \n",
              "818       1 year          No       No  135990  3 stars                  0   \n",
              "819       1 year          No       No  144990  3 stars                  0   \n",
              "820       1 year          No       No  149990  3 stars                  0   \n",
              "821       1 year          No       No  142990  3 stars                  0   \n",
              "822  No warranty          No       No   57490  4 stars                 18   \n",
              "\n",
              "     Number of Reviews  \n",
              "0                    0  \n",
              "1                    5  \n",
              "2                    1  \n",
              "3                    0  \n",
              "4                    0  \n",
              "..                 ...  \n",
              "818                  0  \n",
              "819                  0  \n",
              "820                  0  \n",
              "821                  0  \n",
              "822                  4  \n",
              "\n",
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              "      <td>Lenovo</td>\n",
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              "      <td>10th</td>\n",
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              "      <td>No warranty</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>38999</td>\n",
              "      <td>3 stars</td>\n",
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              "      <td>Lenovo</td>\n",
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              "      <td>Core i3</td>\n",
              "      <td>10th</td>\n",
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              "      <td>Windows</td>\n",
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              "      <td>0 GB</td>\n",
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              "      <td>No warranty</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>39999</td>\n",
              "      <td>3 stars</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
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              "      <th>3</th>\n",
              "      <td>ASUS</td>\n",
              "      <td>Intel</td>\n",
              "      <td>Core i5</td>\n",
              "      <td>10th</td>\n",
              "      <td>8 GB</td>\n",
              "      <td>DDR4</td>\n",
              "      <td>512 GB</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Windows</td>\n",
              "      <td>32-bit</td>\n",
              "      <td>2 GB</td>\n",
              "      <td>Casual</td>\n",
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              "      <td>No</td>\n",
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              "      <td>No</td>\n",
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              "      <td>3 stars</td>\n",
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              "      <td>ASUS</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 823,\n  \"fields\": [\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Lenovo\",\n          \"DELL\",\n          \"ASUS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"processor_brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Intel\",\n          \"AMD\",\n          \"M1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"processor_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Core i9\",\n          \"Core i3\",\n          \"Ryzen 7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"processor_gnrtn\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Not Available\",\n          \"9th\",\n          \"10th\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ram_gb\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"8 GB\",\n          \"32 GB\",\n          \"4 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ram_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"DDR4\",\n          \"LPDDR4\",\n          \"LPDDR3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ssd\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"0 GB\",\n          \"512 GB\",\n          \"2048 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hdd\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"0 GB\",\n          \"2048 GB\",\n          \"1024 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"os\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Windows\",\n          \"DOS\",\n          \"Mac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"os_bit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"32-bit\",\n          \"64-bit\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"graphic_card_gb\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2 GB\",\n          \"8 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Casual\",\n          \"ThinNlight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"warranty\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1 year\",\n          \"3 years\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Touchscreen\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"msoffice\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45101,\n        \"min\": 16990,\n        \"max\": 441990,\n        \"num_unique_values\": 405,\n        \"samples\": [\n          44890,\n          86999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"3 stars\",\n          \"1 star\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Ratings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1047,\n        \"min\": 0,\n        \"max\": 15279,\n        \"num_unique_values\": 282,\n        \"samples\": [\n          15,\n          527\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Reviews\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 121,\n        \"min\": 0,\n        \"max\": 1947,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          48,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "df = pd.read_csv(\"laptopPrice.csv\")\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sWUVWWG0hirf"
      },
      "source": [
        "**Data Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 101,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "jDfJ8sZLhirf",
        "outputId": "6f82079d-7e5a-4630-9753-e2a9ee9728b7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(823, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "zAF_gnv1hirg",
        "outputId": "a938942b-b720-4ceb-e8ad-2d72af61689c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['brand', 'processor_brand', 'processor_name', 'processor_gnrtn',\n",
              "       'ram_gb', 'ram_type', 'ssd', 'hdd', 'os', 'os_bit', 'graphic_card_gb',\n",
              "       'weight', 'warranty', 'Touchscreen', 'msoffice', 'Price', 'rating',\n",
              "       'Number of Ratings', 'Number of Reviews'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "YvgbNeFniKsC",
        "outputId": "86735d64-9cba-4ff8-87c7-acd812119f5a"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      brand processor_brand processor_name processor_gnrtn ram_gb ram_type  \\\n",
              "0      ASUS           Intel        Core i3            10th   4 GB     DDR4   \n",
              "1    Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
              "2    Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
              "3      ASUS           Intel        Core i5            10th   8 GB     DDR4   \n",
              "4      ASUS           Intel   Celeron Dual   Not Available   4 GB     DDR4   \n",
              "..      ...             ...            ...             ...    ...      ...   \n",
              "818    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "819    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "820    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "821    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "822  Lenovo             AMD        Ryzen 5            10th   8 GB     DDR4   \n",
              "\n",
              "         ssd      hdd       os  os_bit graphic_card_gb      weight  \\\n",
              "0       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "1       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "2       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "3     512 GB     0 GB  Windows  32-bit            2 GB      Casual   \n",
              "4       0 GB   512 GB  Windows  64-bit            0 GB      Casual   \n",
              "..       ...      ...      ...     ...             ...         ...   \n",
              "818  1024 GB     0 GB  Windows  64-bit            0 GB      Casual   \n",
              "819  1024 GB     0 GB  Windows  64-bit            0 GB      Casual   \n",
              "820  1024 GB     0 GB  Windows  64-bit            4 GB      Casual   \n",
              "821  1024 GB     0 GB  Windows  64-bit            4 GB      Casual   \n",
              "822   512 GB     0 GB      DOS  64-bit            0 GB  ThinNlight   \n",
              "\n",
              "        warranty Touchscreen msoffice   Price   rating  Number of Ratings  \\\n",
              "0    No warranty          No       No   34649  2 stars                  3   \n",
              "1    No warranty          No       No   38999  3 stars                 65   \n",
              "2    No warranty          No       No   39999  3 stars                  8   \n",
              "3    No warranty          No       No   69990  3 stars                  0   \n",
              "4    No warranty          No       No   26990  3 stars                  0   \n",
              "..           ...         ...      ...     ...      ...                ...   \n",
              "818       1 year          No       No  135990  3 stars                  0   \n",
              "819       1 year          No       No  144990  3 stars                  0   \n",
              "820       1 year          No       No  149990  3 stars                  0   \n",
              "821       1 year          No       No  142990  3 stars                  0   \n",
              "822  No warranty          No       No   57490  4 stars                 18   \n",
              "\n",
              "     Number of Reviews  \n",
              "0                    0  \n",
              "1                    5  \n",
              "2                    1  \n",
              "3                    0  \n",
              "4                    0  \n",
              "..                 ...  \n",
              "818                  0  \n",
              "819                  0  \n",
              "820                  0  \n",
              "821                  0  \n",
              "822                  4  \n",
              "\n",
              "[823 rows x 19 columns]"
            ],
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              "      <th></th>\n",
              "      <th>brand</th>\n",
              "      <th>processor_brand</th>\n",
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              "      <th>msoffice</th>\n",
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              "      <th>Number of Ratings</th>\n",
              "      <th>Number of Reviews</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ASUS</td>\n",
              "      <td>Intel</td>\n",
              "      <td>Core i3</td>\n",
              "      <td>10th</td>\n",
              "      <td>4 GB</td>\n",
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              "      <td>1024 GB</td>\n",
              "      <td>Windows</td>\n",
              "      <td>64-bit</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Casual</td>\n",
              "      <td>No warranty</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>34649</td>\n",
              "      <td>2 stars</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Intel</td>\n",
              "      <td>Core i3</td>\n",
              "      <td>10th</td>\n",
              "      <td>4 GB</td>\n",
              "      <td>DDR4</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>1024 GB</td>\n",
              "      <td>Windows</td>\n",
              "      <td>64-bit</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Casual</td>\n",
              "      <td>No warranty</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>38999</td>\n",
              "      <td>3 stars</td>\n",
              "      <td>65</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lenovo</td>\n",
              "      <td>Intel</td>\n",
              "      <td>Core i3</td>\n",
              "      <td>10th</td>\n",
              "      <td>4 GB</td>\n",
              "      <td>DDR4</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>1024 GB</td>\n",
              "      <td>Windows</td>\n",
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              "      <td>0 GB</td>\n",
              "      <td>Casual</td>\n",
              "      <td>No warranty</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>39999</td>\n",
              "      <td>3 stars</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ASUS</td>\n",
              "      <td>Intel</td>\n",
              "      <td>Core i5</td>\n",
              "      <td>10th</td>\n",
              "      <td>8 GB</td>\n",
              "      <td>DDR4</td>\n",
              "      <td>512 GB</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Windows</td>\n",
              "      <td>32-bit</td>\n",
              "      <td>2 GB</td>\n",
              "      <td>Casual</td>\n",
              "      <td>No warranty</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>69990</td>\n",
              "      <td>3 stars</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ASUS</td>\n",
              "      <td>Intel</td>\n",
              "      <td>Celeron Dual</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>4 GB</td>\n",
              "      <td>DDR4</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>512 GB</td>\n",
              "      <td>Windows</td>\n",
              "      <td>64-bit</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Casual</td>\n",
              "      <td>No warranty</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>26990</td>\n",
              "      <td>3 stars</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>818</th>\n",
              "      <td>ASUS</td>\n",
              "      <td>AMD</td>\n",
              "      <td>Ryzen 9</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>4 GB</td>\n",
              "      <td>DDR4</td>\n",
              "      <td>1024 GB</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Windows</td>\n",
              "      <td>64-bit</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Casual</td>\n",
              "      <td>1 year</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>135990</td>\n",
              "      <td>3 stars</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>819</th>\n",
              "      <td>ASUS</td>\n",
              "      <td>AMD</td>\n",
              "      <td>Ryzen 9</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>4 GB</td>\n",
              "      <td>DDR4</td>\n",
              "      <td>1024 GB</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Windows</td>\n",
              "      <td>64-bit</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Casual</td>\n",
              "      <td>1 year</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>144990</td>\n",
              "      <td>3 stars</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820</th>\n",
              "      <td>ASUS</td>\n",
              "      <td>AMD</td>\n",
              "      <td>Ryzen 9</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>4 GB</td>\n",
              "      <td>DDR4</td>\n",
              "      <td>1024 GB</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Windows</td>\n",
              "      <td>64-bit</td>\n",
              "      <td>4 GB</td>\n",
              "      <td>Casual</td>\n",
              "      <td>1 year</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>149990</td>\n",
              "      <td>3 stars</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>ASUS</td>\n",
              "      <td>AMD</td>\n",
              "      <td>Ryzen 9</td>\n",
              "      <td>Not Available</td>\n",
              "      <td>4 GB</td>\n",
              "      <td>DDR4</td>\n",
              "      <td>1024 GB</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>Windows</td>\n",
              "      <td>64-bit</td>\n",
              "      <td>4 GB</td>\n",
              "      <td>Casual</td>\n",
              "      <td>1 year</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>142990</td>\n",
              "      <td>3 stars</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>822</th>\n",
              "      <td>Lenovo</td>\n",
              "      <td>AMD</td>\n",
              "      <td>Ryzen 5</td>\n",
              "      <td>10th</td>\n",
              "      <td>8 GB</td>\n",
              "      <td>DDR4</td>\n",
              "      <td>512 GB</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>DOS</td>\n",
              "      <td>64-bit</td>\n",
              "      <td>0 GB</td>\n",
              "      <td>ThinNlight</td>\n",
              "      <td>No warranty</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>57490</td>\n",
              "      <td>4 stars</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>823 rows × 19 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-05b582d0-8843-483e-af7e-cf387c785e3a')\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-05b582d0-8843-483e-af7e-cf387c785e3a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a5f967fb-5908-4ec2-ae82-497b95bd65fa')\"\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --fill-color: #D2E3FC;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a5f967fb-5908-4ec2-ae82-497b95bd65fa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 823,\n  \"fields\": [\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Lenovo\",\n          \"DELL\",\n          \"ASUS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"processor_brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Intel\",\n          \"AMD\",\n          \"M1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"processor_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Core i9\",\n          \"Core i3\",\n          \"Ryzen 7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"processor_gnrtn\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Not Available\",\n          \"9th\",\n          \"10th\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ram_gb\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"8 GB\",\n          \"32 GB\",\n          \"4 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ram_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"DDR4\",\n          \"LPDDR4\",\n          \"LPDDR3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ssd\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"0 GB\",\n          \"512 GB\",\n          \"2048 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hdd\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"0 GB\",\n          \"2048 GB\",\n          \"1024 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"os\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Windows\",\n          \"DOS\",\n          \"Mac\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"os_bit\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"32-bit\",\n          \"64-bit\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"graphic_card_gb\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2 GB\",\n          \"8 GB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weight\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Casual\",\n          \"ThinNlight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"warranty\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"1 year\",\n          \"3 years\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Touchscreen\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"msoffice\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Yes\",\n          \"No\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45101,\n        \"min\": 16990,\n        \"max\": 441990,\n        \"num_unique_values\": 405,\n        \"samples\": [\n          44890,\n          86999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rating\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"3 stars\",\n          \"1 star\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Ratings\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1047,\n        \"min\": 0,\n        \"max\": 15279,\n        \"num_unique_values\": 282,\n        \"samples\": [\n          15,\n          527\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Number of Reviews\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 121,\n        \"min\": 0,\n        \"max\": 1947,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          48,\n          35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "toX6UDfMhirg",
        "outputId": "8254ab0f-9ff3-40d2-bbe0-43d585a9fb23"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.info of       brand processor_brand processor_name processor_gnrtn ram_gb ram_type  \\\n",
              "0      ASUS           Intel        Core i3            10th   4 GB     DDR4   \n",
              "1    Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
              "2    Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
              "3      ASUS           Intel        Core i5            10th   8 GB     DDR4   \n",
              "4      ASUS           Intel   Celeron Dual   Not Available   4 GB     DDR4   \n",
              "..      ...             ...            ...             ...    ...      ...   \n",
              "818    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "819    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "820    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "821    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "822  Lenovo             AMD        Ryzen 5            10th   8 GB     DDR4   \n",
              "\n",
              "         ssd      hdd       os  os_bit graphic_card_gb      weight  \\\n",
              "0       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "1       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "2       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "3     512 GB     0 GB  Windows  32-bit            2 GB      Casual   \n",
              "4       0 GB   512 GB  Windows  64-bit            0 GB      Casual   \n",
              "..       ...      ...      ...     ...             ...         ...   \n",
              "818  1024 GB     0 GB  Windows  64-bit            0 GB      Casual   \n",
              "819  1024 GB     0 GB  Windows  64-bit            0 GB      Casual   \n",
              "820  1024 GB     0 GB  Windows  64-bit            4 GB      Casual   \n",
              "821  1024 GB     0 GB  Windows  64-bit            4 GB      Casual   \n",
              "822   512 GB     0 GB      DOS  64-bit            0 GB  ThinNlight   \n",
              "\n",
              "        warranty Touchscreen msoffice   Price   rating  Number of Ratings  \\\n",
              "0    No warranty          No       No   34649  2 stars                  3   \n",
              "1    No warranty          No       No   38999  3 stars                 65   \n",
              "2    No warranty          No       No   39999  3 stars                  8   \n",
              "3    No warranty          No       No   69990  3 stars                  0   \n",
              "4    No warranty          No       No   26990  3 stars                  0   \n",
              "..           ...         ...      ...     ...      ...                ...   \n",
              "818       1 year          No       No  135990  3 stars                  0   \n",
              "819       1 year          No       No  144990  3 stars                  0   \n",
              "820       1 year          No       No  149990  3 stars                  0   \n",
              "821       1 year          No       No  142990  3 stars                  0   \n",
              "822  No warranty          No       No   57490  4 stars                 18   \n",
              "\n",
              "     Number of Reviews  \n",
              "0                    0  \n",
              "1                    5  \n",
              "2                    1  \n",
              "3                    0  \n",
              "4                    0  \n",
              "..                 ...  \n",
              "818                  0  \n",
              "819                  0  \n",
              "820                  0  \n",
              "821                  0  \n",
              "822                  4  \n",
              "\n",
              "[823 rows x 19 columns]>"
            ],
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame.info</b><br/>def info(verbose: bool | None=None, buf: WriteBuffer[str] | None=None, max_cols: int | None=None, memory_usage: bool | str | None=None, show_counts: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Print a concise summary of a DataFrame.\n",
              "\n",
              "This method prints information about a DataFrame including\n",
              "the index dtype and columns, non-null values and memory usage.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "verbose : bool, optional\n",
              "    Whether to print the full summary. By default, the setting in\n",
              "    ``pandas.options.display.max_info_columns`` is followed.\n",
              "buf : writable buffer, defaults to sys.stdout\n",
              "    Where to send the output. By default, the output is printed to\n",
              "    sys.stdout. Pass a writable buffer if you need to further process\n",
              "    the output.\n",
              "max_cols : int, optional\n",
              "    When to switch from the verbose to the truncated output. If the\n",
              "    DataFrame has more than `max_cols` columns, the truncated output\n",
              "    is used. By default, the setting in\n",
              "    ``pandas.options.display.max_info_columns`` is used.\n",
              "memory_usage : bool, str, optional\n",
              "    Specifies whether total memory usage of the DataFrame\n",
              "    elements (including the index) should be displayed. By default,\n",
              "    this follows the ``pandas.options.display.memory_usage`` setting.\n",
              "\n",
              "    True always show memory usage. False never shows memory usage.\n",
              "    A value of &#x27;deep&#x27; is equivalent to &quot;True with deep introspection&quot;.\n",
              "    Memory usage is shown in human-readable units (base-2\n",
              "    representation). Without deep introspection a memory estimation is\n",
              "    made based in column dtype and number of rows assuming values\n",
              "    consume the same memory amount for corresponding dtypes. With deep\n",
              "    memory introspection, a real memory usage calculation is performed\n",
              "    at the cost of computational resources. See the\n",
              "    :ref:`Frequently Asked Questions &lt;df-memory-usage&gt;` for more\n",
              "    details.\n",
              "show_counts : bool, optional\n",
              "    Whether to show the non-null counts. By default, this is shown\n",
              "    only if the DataFrame is smaller than\n",
              "    ``pandas.options.display.max_info_rows`` and\n",
              "    ``pandas.options.display.max_info_columns``. A value of True always\n",
              "    shows the counts, and False never shows the counts.\n",
              "\n",
              "Returns\n",
              "-------\n",
              "None\n",
              "    This method prints a summary of a DataFrame and returns None.\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.describe: Generate descriptive statistics of DataFrame\n",
              "    columns.\n",
              "DataFrame.memory_usage: Memory usage of DataFrame columns.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "&gt;&gt;&gt; int_values = [1, 2, 3, 4, 5]\n",
              "&gt;&gt;&gt; text_values = [&#x27;alpha&#x27;, &#x27;beta&#x27;, &#x27;gamma&#x27;, &#x27;delta&#x27;, &#x27;epsilon&#x27;]\n",
              "&gt;&gt;&gt; float_values = [0.0, 0.25, 0.5, 0.75, 1.0]\n",
              "&gt;&gt;&gt; df = pd.DataFrame({&quot;int_col&quot;: int_values, &quot;text_col&quot;: text_values,\n",
              "...                   &quot;float_col&quot;: float_values})\n",
              "&gt;&gt;&gt; df\n",
              "    int_col text_col  float_col\n",
              "0        1    alpha       0.00\n",
              "1        2     beta       0.25\n",
              "2        3    gamma       0.50\n",
              "3        4    delta       0.75\n",
              "4        5  epsilon       1.00\n",
              "\n",
              "Prints information of all columns:\n",
              "\n",
              "&gt;&gt;&gt; df.info(verbose=True)\n",
              "&lt;class &#x27;pandas.core.frame.DataFrame&#x27;&gt;\n",
              "RangeIndex: 5 entries, 0 to 4\n",
              "Data columns (total 3 columns):\n",
              " #   Column     Non-Null Count  Dtype\n",
              "---  ------     --------------  -----\n",
              " 0   int_col    5 non-null      int64\n",
              " 1   text_col   5 non-null      object\n",
              " 2   float_col  5 non-null      float64\n",
              "dtypes: float64(1), int64(1), object(1)\n",
              "memory usage: 248.0+ bytes\n",
              "\n",
              "Prints a summary of columns count and its dtypes but not per column\n",
              "information:\n",
              "\n",
              "&gt;&gt;&gt; df.info(verbose=False)\n",
              "&lt;class &#x27;pandas.core.frame.DataFrame&#x27;&gt;\n",
              "RangeIndex: 5 entries, 0 to 4\n",
              "Columns: 3 entries, int_col to float_col\n",
              "dtypes: float64(1), int64(1), object(1)\n",
              "memory usage: 248.0+ bytes\n",
              "\n",
              "Pipe output of DataFrame.info to buffer instead of sys.stdout, get\n",
              "buffer content and writes to a text file:\n",
              "\n",
              "&gt;&gt;&gt; import io\n",
              "&gt;&gt;&gt; buffer = io.StringIO()\n",
              "&gt;&gt;&gt; df.info(buf=buffer)\n",
              "&gt;&gt;&gt; s = buffer.getvalue()\n",
              "&gt;&gt;&gt; with open(&quot;df_info.txt&quot;, &quot;w&quot;,\n",
              "...           encoding=&quot;utf-8&quot;) as f:  # doctest: +SKIP\n",
              "...     f.write(s)\n",
              "260\n",
              "\n",
              "The `memory_usage` parameter allows deep introspection mode, specially\n",
              "useful for big DataFrames and fine-tune memory optimization:\n",
              "\n",
              "&gt;&gt;&gt; random_strings_array = np.random.choice([&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;], 10 ** 6)\n",
              "&gt;&gt;&gt; df = pd.DataFrame({\n",
              "...     &#x27;column_1&#x27;: np.random.choice([&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;], 10 ** 6),\n",
              "...     &#x27;column_2&#x27;: np.random.choice([&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;], 10 ** 6),\n",
              "...     &#x27;column_3&#x27;: np.random.choice([&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;], 10 ** 6)\n",
              "... })\n",
              "&gt;&gt;&gt; df.info()\n",
              "&lt;class &#x27;pandas.core.frame.DataFrame&#x27;&gt;\n",
              "RangeIndex: 1000000 entries, 0 to 999999\n",
              "Data columns (total 3 columns):\n",
              " #   Column    Non-Null Count    Dtype\n",
              "---  ------    --------------    -----\n",
              " 0   column_1  1000000 non-null  object\n",
              " 1   column_2  1000000 non-null  object\n",
              " 2   column_3  1000000 non-null  object\n",
              "dtypes: object(3)\n",
              "memory usage: 22.9+ MB\n",
              "\n",
              "&gt;&gt;&gt; df.info(memory_usage=&#x27;deep&#x27;)\n",
              "&lt;class &#x27;pandas.core.frame.DataFrame&#x27;&gt;\n",
              "RangeIndex: 1000000 entries, 0 to 999999\n",
              "Data columns (total 3 columns):\n",
              " #   Column    Non-Null Count    Dtype\n",
              "---  ------    --------------    -----\n",
              " 0   column_1  1000000 non-null  object\n",
              " 1   column_2  1000000 non-null  object\n",
              " 2   column_3  1000000 non-null  object\n",
              "dtypes: object(3)\n",
              "memory usage: 165.9 MB</pre>\n",
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        "df.info"
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            "text/plain": [
              "<bound method NDFrame.describe of       brand processor_brand processor_name processor_gnrtn ram_gb ram_type  \\\n",
              "0      ASUS           Intel        Core i3            10th   4 GB     DDR4   \n",
              "1    Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
              "2    Lenovo           Intel        Core i3            10th   4 GB     DDR4   \n",
              "3      ASUS           Intel        Core i5            10th   8 GB     DDR4   \n",
              "4      ASUS           Intel   Celeron Dual   Not Available   4 GB     DDR4   \n",
              "..      ...             ...            ...             ...    ...      ...   \n",
              "818    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "819    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "820    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "821    ASUS             AMD        Ryzen 9   Not Available   4 GB     DDR4   \n",
              "822  Lenovo             AMD        Ryzen 5            10th   8 GB     DDR4   \n",
              "\n",
              "         ssd      hdd       os  os_bit graphic_card_gb      weight  \\\n",
              "0       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "1       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "2       0 GB  1024 GB  Windows  64-bit            0 GB      Casual   \n",
              "3     512 GB     0 GB  Windows  32-bit            2 GB      Casual   \n",
              "4       0 GB   512 GB  Windows  64-bit            0 GB      Casual   \n",
              "..       ...      ...      ...     ...             ...         ...   \n",
              "818  1024 GB     0 GB  Windows  64-bit            0 GB      Casual   \n",
              "819  1024 GB     0 GB  Windows  64-bit            0 GB      Casual   \n",
              "820  1024 GB     0 GB  Windows  64-bit            4 GB      Casual   \n",
              "821  1024 GB     0 GB  Windows  64-bit            4 GB      Casual   \n",
              "822   512 GB     0 GB      DOS  64-bit            0 GB  ThinNlight   \n",
              "\n",
              "        warranty Touchscreen msoffice   Price   rating  Number of Ratings  \\\n",
              "0    No warranty          No       No   34649  2 stars                  3   \n",
              "1    No warranty          No       No   38999  3 stars                 65   \n",
              "2    No warranty          No       No   39999  3 stars                  8   \n",
              "3    No warranty          No       No   69990  3 stars                  0   \n",
              "4    No warranty          No       No   26990  3 stars                  0   \n",
              "..           ...         ...      ...     ...      ...                ...   \n",
              "818       1 year          No       No  135990  3 stars                  0   \n",
              "819       1 year          No       No  144990  3 stars                  0   \n",
              "820       1 year          No       No  149990  3 stars                  0   \n",
              "821       1 year          No       No  142990  3 stars                  0   \n",
              "822  No warranty          No       No   57490  4 stars                 18   \n",
              "\n",
              "     Number of Reviews  \n",
              "0                    0  \n",
              "1                    5  \n",
              "2                    1  \n",
              "3                    0  \n",
              "4                    0  \n",
              "..                 ...  \n",
              "818                  0  \n",
              "819                  0  \n",
              "820                  0  \n",
              "821                  0  \n",
              "822                  4  \n",
              "\n",
              "[823 rows x 19 columns]>"
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              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
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              "\n",
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              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.generic.NDFrame.describe</b><br/>def describe(percentiles=None, include=None, exclude=None) -&gt; NDFrameT</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py</a>Generate descriptive statistics.\n",
              "\n",
              "Descriptive statistics include those that summarize the central\n",
              "tendency, dispersion and shape of a\n",
              "dataset&#x27;s distribution, excluding ``NaN`` values.\n",
              "\n",
              "Analyzes both numeric and object series, as well\n",
              "as ``DataFrame`` column sets of mixed data types. The output\n",
              "will vary depending on what is provided. Refer to the notes\n",
              "below for more detail.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "percentiles : list-like of numbers, optional\n",
              "    The percentiles to include in the output. All should\n",
              "    fall between 0 and 1. The default is\n",
              "    ``[.25, .5, .75]``, which returns the 25th, 50th, and\n",
              "    75th percentiles.\n",
              "include : &#x27;all&#x27;, list-like of dtypes or None (default), optional\n",
              "    A white list of data types to include in the result. Ignored\n",
              "    for ``Series``. Here are the options:\n",
              "\n",
              "    - &#x27;all&#x27; : All columns of the input will be included in the output.\n",
              "    - A list-like of dtypes : Limits the results to the\n",
              "      provided data types.\n",
              "      To limit the result to numeric types submit\n",
              "      ``numpy.number``. To limit it instead to object columns submit\n",
              "      the ``numpy.object`` data type. Strings\n",
              "      can also be used in the style of\n",
              "      ``select_dtypes`` (e.g. ``df.describe(include=[&#x27;O&#x27;])``). To\n",
              "      select pandas categorical columns, use ``&#x27;category&#x27;``\n",
              "    - None (default) : The result will include all numeric columns.\n",
              "exclude : list-like of dtypes or None (default), optional,\n",
              "    A black list of data types to omit from the result. Ignored\n",
              "    for ``Series``. Here are the options:\n",
              "\n",
              "    - A list-like of dtypes : Excludes the provided data types\n",
              "      from the result. To exclude numeric types submit\n",
              "      ``numpy.number``. To exclude object columns submit the data\n",
              "      type ``numpy.object``. Strings can also be used in the style of\n",
              "      ``select_dtypes`` (e.g. ``df.describe(exclude=[&#x27;O&#x27;])``). To\n",
              "      exclude pandas categorical columns, use ``&#x27;category&#x27;``\n",
              "    - None (default) : The result will exclude nothing.\n",
              "\n",
              "Returns\n",
              "-------\n",
              "Series or DataFrame\n",
              "    Summary statistics of the Series or Dataframe provided.\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.count: Count number of non-NA/null observations.\n",
              "DataFrame.max: Maximum of the values in the object.\n",
              "DataFrame.min: Minimum of the values in the object.\n",
              "DataFrame.mean: Mean of the values.\n",
              "DataFrame.std: Standard deviation of the observations.\n",
              "DataFrame.select_dtypes: Subset of a DataFrame including/excluding\n",
              "    columns based on their dtype.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "For numeric data, the result&#x27;s index will include ``count``,\n",
              "``mean``, ``std``, ``min``, ``max`` as well as lower, ``50`` and\n",
              "upper percentiles. By default the lower percentile is ``25`` and the\n",
              "upper percentile is ``75``. The ``50`` percentile is the\n",
              "same as the median.\n",
              "\n",
              "For object data (e.g. strings or timestamps), the result&#x27;s index\n",
              "will include ``count``, ``unique``, ``top``, and ``freq``. The ``top``\n",
              "is the most common value. The ``freq`` is the most common value&#x27;s\n",
              "frequency. Timestamps also include the ``first`` and ``last`` items.\n",
              "\n",
              "If multiple object values have the highest count, then the\n",
              "``count`` and ``top`` results will be arbitrarily chosen from\n",
              "among those with the highest count.\n",
              "\n",
              "For mixed data types provided via a ``DataFrame``, the default is to\n",
              "return only an analysis of numeric columns. If the dataframe consists\n",
              "only of object and categorical data without any numeric columns, the\n",
              "default is to return an analysis of both the object and categorical\n",
              "columns. If ``include=&#x27;all&#x27;`` is provided as an option, the result\n",
              "will include a union of attributes of each type.\n",
              "\n",
              "The `include` and `exclude` parameters can be used to limit\n",
              "which columns in a ``DataFrame`` are analyzed for the output.\n",
              "The parameters are ignored when analyzing a ``Series``.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Describing a numeric ``Series``.\n",
              "\n",
              "&gt;&gt;&gt; s = pd.Series([1, 2, 3])\n",
              "&gt;&gt;&gt; s.describe()\n",
              "count    3.0\n",
              "mean     2.0\n",
              "std      1.0\n",
              "min      1.0\n",
              "25%      1.5\n",
              "50%      2.0\n",
              "75%      2.5\n",
              "max      3.0\n",
              "dtype: float64\n",
              "\n",
              "Describing a categorical ``Series``.\n",
              "\n",
              "&gt;&gt;&gt; s = pd.Series([&#x27;a&#x27;, &#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; s.describe()\n",
              "count     4\n",
              "unique    3\n",
              "top       a\n",
              "freq      2\n",
              "dtype: object\n",
              "\n",
              "Describing a timestamp ``Series``.\n",
              "\n",
              "&gt;&gt;&gt; s = pd.Series([\n",
              "...     np.datetime64(&quot;2000-01-01&quot;),\n",
              "...     np.datetime64(&quot;2010-01-01&quot;),\n",
              "...     np.datetime64(&quot;2010-01-01&quot;)\n",
              "... ])\n",
              "&gt;&gt;&gt; s.describe()\n",
              "count                      3\n",
              "mean     2006-09-01 08:00:00\n",
              "min      2000-01-01 00:00:00\n",
              "25%      2004-12-31 12:00:00\n",
              "50%      2010-01-01 00:00:00\n",
              "75%      2010-01-01 00:00:00\n",
              "max      2010-01-01 00:00:00\n",
              "dtype: object\n",
              "\n",
              "Describing a ``DataFrame``. By default only numeric fields\n",
              "are returned.\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame({&#x27;categorical&#x27;: pd.Categorical([&#x27;d&#x27;,&#x27;e&#x27;,&#x27;f&#x27;]),\n",
              "...                    &#x27;numeric&#x27;: [1, 2, 3],\n",
              "...                    &#x27;object&#x27;: [&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;]\n",
              "...                   })\n",
              "&gt;&gt;&gt; df.describe()\n",
              "       numeric\n",
              "count      3.0\n",
              "mean       2.0\n",
              "std        1.0\n",
              "min        1.0\n",
              "25%        1.5\n",
              "50%        2.0\n",
              "75%        2.5\n",
              "max        3.0\n",
              "\n",
              "Describing all columns of a ``DataFrame`` regardless of data type.\n",
              "\n",
              "&gt;&gt;&gt; df.describe(include=&#x27;all&#x27;)  # doctest: +SKIP\n",
              "       categorical  numeric object\n",
              "count            3      3.0      3\n",
              "unique           3      NaN      3\n",
              "top              f      NaN      a\n",
              "freq             1      NaN      1\n",
              "mean           NaN      2.0    NaN\n",
              "std            NaN      1.0    NaN\n",
              "min            NaN      1.0    NaN\n",
              "25%            NaN      1.5    NaN\n",
              "50%            NaN      2.0    NaN\n",
              "75%            NaN      2.5    NaN\n",
              "max            NaN      3.0    NaN\n",
              "\n",
              "Describing a column from a ``DataFrame`` by accessing it as\n",
              "an attribute.\n",
              "\n",
              "&gt;&gt;&gt; df.numeric.describe()\n",
              "count    3.0\n",
              "mean     2.0\n",
              "std      1.0\n",
              "min      1.0\n",
              "25%      1.5\n",
              "50%      2.0\n",
              "75%      2.5\n",
              "max      3.0\n",
              "Name: numeric, dtype: float64\n",
              "\n",
              "Including only numeric columns in a ``DataFrame`` description.\n",
              "\n",
              "&gt;&gt;&gt; df.describe(include=[np.number])\n",
              "       numeric\n",
              "count      3.0\n",
              "mean       2.0\n",
              "std        1.0\n",
              "min        1.0\n",
              "25%        1.5\n",
              "50%        2.0\n",
              "75%        2.5\n",
              "max        3.0\n",
              "\n",
              "Including only string columns in a ``DataFrame`` description.\n",
              "\n",
              "&gt;&gt;&gt; df.describe(include=[object])  # doctest: +SKIP\n",
              "       object\n",
              "count       3\n",
              "unique      3\n",
              "top         a\n",
              "freq        1\n",
              "\n",
              "Including only categorical columns from a ``DataFrame`` description.\n",
              "\n",
              "&gt;&gt;&gt; df.describe(include=[&#x27;category&#x27;])\n",
              "       categorical\n",
              "count            3\n",
              "unique           3\n",
              "top              d\n",
              "freq             1\n",
              "\n",
              "Excluding numeric columns from a ``DataFrame`` description.\n",
              "\n",
              "&gt;&gt;&gt; df.describe(exclude=[np.number])  # doctest: +SKIP\n",
              "       categorical object\n",
              "count            3      3\n",
              "unique           3      3\n",
              "top              f      a\n",
              "freq             1      1\n",
              "\n",
              "Excluding object columns from a ``DataFrame`` description.\n",
              "\n",
              "&gt;&gt;&gt; df.describe(exclude=[object])  # doctest: +SKIP\n",
              "       categorical  numeric\n",
              "count            3      3.0\n",
              "unique           3      NaN\n",
              "top              f      NaN\n",
              "freq             1      NaN\n",
              "mean           NaN      2.0\n",
              "std            NaN      1.0\n",
              "min            NaN      1.0\n",
              "25%            NaN      1.5\n",
              "50%            NaN      2.0\n",
              "75%            NaN      2.5\n",
              "max            NaN      3.0</pre>\n",
              "      <script>\n",
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          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "df.describe"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = df.drop(columns=['Number of Ratings', 'Number of Reviews'])\n",
        "df = pd.get_dummies(data)\n"
      ],
      "metadata": {
        "id": "HKMKHNkUnOyA"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "QOjgniv7hirh",
        "outputId": "1f88407c-255f-4faf-bb2c-3c58d6606ab1"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Price  brand_APPLE  brand_ASUS  brand_Avita  brand_DELL  brand_HP  \\\n",
              "0    False        False       False        False       False     False   \n",
              "1    False        False       False        False       False     False   \n",
              "2    False        False       False        False       False     False   \n",
              "3    False        False       False        False       False     False   \n",
              "4    False        False       False        False       False     False   \n",
              "..     ...          ...         ...          ...         ...       ...   \n",
              "818  False        False       False        False       False     False   \n",
              "819  False        False       False        False       False     False   \n",
              "820  False        False       False        False       False     False   \n",
              "821  False        False       False        False       False     False   \n",
              "822  False        False       False        False       False     False   \n",
              "\n",
              "     brand_Lenovo  brand_MSI  brand_acer  processor_brand_AMD  ...  \\\n",
              "0           False      False       False                False  ...   \n",
              "1           False      False       False                False  ...   \n",
              "2           False      False       False                False  ...   \n",
              "3           False      False       False                False  ...   \n",
              "4           False      False       False                False  ...   \n",
              "..            ...        ...         ...                  ...  ...   \n",
              "818         False      False       False                False  ...   \n",
              "819         False      False       False                False  ...   \n",
              "820         False      False       False                False  ...   \n",
              "821         False      False       False                False  ...   \n",
              "822         False      False       False                False  ...   \n",
              "\n",
              "     warranty_No warranty  Touchscreen_No  Touchscreen_Yes  msoffice_No  \\\n",
              "0                   False           False            False        False   \n",
              "1                   False           False            False        False   \n",
              "2                   False           False            False        False   \n",
              "3                   False           False            False        False   \n",
              "4                   False           False            False        False   \n",
              "..                    ...             ...              ...          ...   \n",
              "818                 False           False            False        False   \n",
              "819                 False           False            False        False   \n",
              "820                 False           False            False        False   \n",
              "821                 False           False            False        False   \n",
              "822                 False           False            False        False   \n",
              "\n",
              "     msoffice_Yes  rating_1 star  rating_2 stars  rating_3 stars  \\\n",
              "0           False          False           False           False   \n",
              "1           False          False           False           False   \n",
              "2           False          False           False           False   \n",
              "3           False          False           False           False   \n",
              "4           False          False           False           False   \n",
              "..            ...            ...             ...             ...   \n",
              "818         False          False           False           False   \n",
              "819         False          False           False           False   \n",
              "820         False          False           False           False   \n",
              "821         False          False           False           False   \n",
              "822         False          False           False           False   \n",
              "\n",
              "     rating_4 stars  rating_5 stars  \n",
              "0             False           False  \n",
              "1             False           False  \n",
              "2             False           False  \n",
              "3             False           False  \n",
              "4             False           False  \n",
              "..              ...             ...  \n",
              "818           False           False  \n",
              "819           False           False  \n",
              "820           False           False  \n",
              "821           False           False  \n",
              "822           False           False  \n",
              "\n",
              "[823 rows x 78 columns]"
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              "      <td>False</td>\n",
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              "        const element = document.querySelector('#df-62bca39e-f448-4f8c-b275-b6558bb543ca');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-83b69183-6c89-44dc-bd6f-fb78e82e9ae3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83b69183-6c89-44dc-bd6f-fb78e82e9ae3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-83b69183-6c89-44dc-bd6f-fb78e82e9ae3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 107
        }
      ],
      "source": [
        "df.isnull()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 108,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "CM_dEFPRhirh",
        "outputId": "7322ea05-d9d4-49f6-d5a3-b95e77939f16"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Price             0\n",
              "brand_APPLE       0\n",
              "brand_ASUS        0\n",
              "brand_Avita       0\n",
              "brand_DELL        0\n",
              "                 ..\n",
              "rating_1 star     0\n",
              "rating_2 stars    0\n",
              "rating_3 stars    0\n",
              "rating_4 stars    0\n",
              "rating_5 stars    0\n",
              "Length: 78, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_eIsHoUthirj"
      },
      "source": [
        "**Exploratory Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "fgzpG4PDhirk",
        "outputId": "49acd22a-e890-4de1-ff4b-27aa4ad503ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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CwkL3lpmZ6dXzExERke/w2bDz888/Izc3F3FxcdBoNNBoNDhx4gSefvppJCQkAAAsFgtyc3M9nme325Gfnw+LxVLrufV6PYxGo8dGREREyuSzFxUcM2YMkpOTPfYNGTIEY8aMwbhx4wAASUlJKCgoQHp6Onr27AkAWLt2LZxOJ/r06dPoNRMREZHvkTXsFBcX4+jRo+7HGRkZ2LlzJ8LCwhAXF4fw8HCP9lqtFhaLBW3btgUAtG/fHkOHDsWECROwYMEC2Gw2pKSkYNSoUVyJRURERABkHsbavn07unfvju7duwMApk6diu7du2PGjBl1Psd//vMftGvXDoMGDcLw4cPRt29ffPTRRw1VMhEREfkZWXt2BgwYACFEndsfP3682r6wsDB88cUXXqyKiIiIlMRnJygTEREReQPDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDThOW0LIVtDrdFbeElq3kLpWIiKjeNHIXQPLJOpWJ2Sv2XrHdsyM6NUI1REREDYM9O0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaAw7REREpGgMO0RERKRoDDtERESkaLKGnY0bN+K2225DTEwMJEnC0qVL3cdsNhuee+45dO7cGUFBQYiJicEDDzyA06dPe5wjPz8fo0ePhtFohNlsxvjx41FcXNzI74SIiIh8laxhp6SkBF27dsW8efOqHSstLcWOHTswffp07NixA9999x0OHTqE22+/3aPd6NGjsW/fPqxevRrLly/Hxo0bMXHixMZ6C0REROTjZL1dxLBhwzBs2LAaj5lMJqxevdpj3wcffIDrr78eJ0+eRFxcHA4cOIBVq1Zh27Zt6NWrFwBg7ty5GD58ON5++23ExMQ0+HsgIiIi3+ZXc3YKCwshSRLMZjMAYMuWLTCbze6gAwDJyclQqVTYunVrreepqKiA1Wr12IiIiEiZ/CbslJeX47nnnsN9990Ho9EIAMjOzkZkZKRHO41Gg7CwMGRnZ9d6rlmzZsFkMrm32NjYBq2diIiI5OMXYcdms+Gvf/0rhBCYP3/+NZ9v2rRpKCwsdG+ZmZleqJKIiIh8kaxzdurCFXROnDiBtWvXunt1AMBisSA3N9ejvd1uR35+PiwWS63n1Ov10Ov1DVYzERER+Q6f7tlxBZ0jR45gzZo1CA8P9zielJSEgoICpKenu/etXbsWTqcTffr0aexyiYiIyAfJ2rNTXFyMo0ePuh9nZGRg586dCAsLQ3R0NO655x7s2LEDy5cvh8PhcM/DCQsLg06nQ/v27TF06FBMmDABCxYsgM1mQ0pKCkaNGsWVWERERARA5rCzfft2DBw40P146tSpAICxY8filVdewbJlywAA3bp183jeunXrMGDAAADAf/7zH6SkpGDQoEFQqVQYOXIk5syZ0yj1ExERke+TNewMGDAAQohaj1/umEtYWBi++OILb5ZFRERECuLTc3aIiIiIrhXDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESkaww4REREpGsMOERERKRrDDhERESmaRu4CqPGdOFeCH/dlw9DmBjicAmqVJHdJREREDYZhpwnJOFuCV5btw4bDeQCA8Nuew783ZaBbrBm9E0IhSQw9RESkPBzGaiKKym0Yt/A3bDicB0kCklqGw1GcjzKbA1uOncPmP87JXSIREVGDYM9OEyCEwPP/3YPj50oRYzLgPxNuQGKzIGj1A/D/zd+IDYfzsP3Eeei1KvSKD5O7XCIiIq9iz04T8PmvJ7BizxloVBI+GN0Dic2Cqg4IJ7rFmtG3dTMAwC9Hz+H4uRIZKyUiIvI+hh2FK620460fDwEAnh/WDj3iQqu16Rkfii7NTQCA9YfyYHc6G7VGIiKihiRr2Nm4cSNuu+02xMTEQJIkLF261OO4EAIzZsxAdHQ0AgICkJycjCNHjni0yc/Px+jRo2E0GmE2mzF+/HgUFxc34rvwbd/tyIK13I6E8EA8dFNire1ubB2OQJ0ahWU27DhZ0HgFEhERNTBZw05JSQm6du2KefPm1Xh89uzZmDNnDhYsWICtW7ciKCgIQ4YMQXl5ubvN6NGjsW/fPqxevRrLly/Hxo0bMXHixMZ6Cz5NCIFPNh8HADyQlADVZZaY6zVq3NymajhrW0Y+rOW2xiiRiIiowck6QXnYsGEYNmxYjceEEEhNTcVLL72EO+64AwDw6aefIioqCkuXLsWoUaNw4MABrFq1Ctu2bUOvXr0AAHPnzsXw4cPx9ttvIyYmptHeiy/a/Mc5HMktRpBOjXt6tbhi+7ZRIdibZUVWQRm2HsvHXzpENUKVREREDctn5+xkZGQgOzsbycnJ7n0mkwl9+vTBli1bAABbtmyB2Wx2Bx0ASE5OhkqlwtatWxu9Zl+z8JfjAIB7eraA0aC9YntJktyTlQ9mW1FcYW/I8oiIiBqFz4ad7OxsAEBUlGfvQlRUlPtYdnY2IiMjPY5rNBqEhYW529SkoqICVqvVY1MaVXAY0g7mAAAeuDGhzs+zmAyIMRvgFMDOzIKGKY6IiKgR+WzYaUizZs2CyWRyb7GxsXKX5HWGxJ4QAugeZ0ariOCrem7PCyu29pwqRIXd0RDlERERNRqfDTsWiwUAkJOT47E/JyfHfcxisSA3N9fjuN1uR35+vrtNTaZNm4bCwkL3lpmZ6eXq5WdIrBrau6Vt5BVaVpfYLAhhgTpUOpzYl6W8Xi8iImpafDbsJCYmwmKxIC0tzb3ParVi69atSEpKAgAkJSWhoKAA6enp7jZr166F0+lEnz59aj23Xq+H0Wj02JTE7nBCH98FADCw3dWHHUmS0CPeDADYeaoAAO+ZRURE/kvW1VjFxcU4evSo+3FGRgZ27tyJsLAwxMXFYcqUKXj11VfRpk0bJCYmYvr06YiJicGdd94JAGjfvj2GDh2KCRMmYMGCBbDZbEhJScGoUaOa9EqsrIIyqLQGRBn16BhTvyDXNioEG4+cRVG5HfrYTl6ukIiIqPHIGna2b9+OgQMHuh9PnToVADB27FgsWrQIzz77LEpKSjBx4kQUFBSgb9++WLVqFQwGg/s5//nPf5CSkoJBgwZBpVJh5MiRmDNnTqO/F1+Scbbqlg8D20bW+07mGrUKbaNCsCerEIGdBnmzPCIiokYla9gZMGAAhBC1HpckCTNnzsTMmTNrbRMWFoYvvviiIcrzS0IIHD9XCqB+Q1gX6xhjxJ6sQgS0uQGFZTaYAq68fJ2IiMjX+OycHaqf86U2FJbZIOw29zVz6isyRI/wIB0kjR7/23XaSxUSERE1LoYdhTmZX9WrU5G1D0H6a+u4kyQJHS7M+Vm8XXkr1oiIqGlg2FGYM4VlAIDKU/u9cr52lhAIhx27ThXiaC5vsEpERP6HYUdhsgurbpJaeeawV84XqNOg4uQuAMAPe8545ZxERESNiWFHQUoq7LCWV93PqjL7iNfOW3Z4MwCGHSIi8k8MOwqSba3q1QkP0kFUlnrtvGVHt0KrlnAwu4hDWURE5HcYdhTkzIUhLIvJcIWWV0dUlOCmCyu72LtDRET+hmFHQbIbKOwAwIjO0QAYdoiIyP8w7CiE0ymQc2EYK9ro/bAzuIOFQ1lEROSXGHYU4mxJBexOAZ1GhbAgndfPbwrUuoeyftqf7fXzExERNRSGHYVwz9cxGup9P6wrSW4fBQBIO5DbIOcnIiJqCAw7CpHTgPN1XAa1r7rX1o6T53GuuKLBXoeIiMibGHYUIu9C+IgK0TfYa0SbAtAxxgghgHWH8hrsdYiIiLyJYUcBHE6B/JJKAECz4IYLOwAwyD2UldOgr0NEROQtDDsKcL60Ek4B6NQqhBiu7eafV5J8YShr4+E8VNgdDfpaRERE3sCwowDniqt6dcKDdQ02OdmlU4wJkSF6lFQ6sPVYfoO+FhERkTcw7CjA2QvzdRp6CAsAVCrJPVGZQ1lEROQPGHYUwBV2woO9f32dmgxoe2Eo68jZRnk9IiKia8GwowBnixtncrLLja3CoVFJyDhbgpPnvHfDUSIioobAsOPnym0OFFfYAQDNGqlnJ8SgRY+4UADAhiNcgk5ERL6NYcfPuSYnhxg00GvUjfa6/a6runXExsMMO0RE5NsYdvxcY05Ovli/6yIAAFv+OAebw9mor01ERHQ1GHb8nHtycgPc/PNyOsWYEBakQ3GFHTtOnG/U1yYiIroaDDt+rrEnJ7uoVBL6XrgL+kbO2yEiIh/GsOPHhLj4NhGN27MD/DmUtfEwl6ATEZHvYtjxYyWVDlQ6nJAAmAK1jf76/dpU9ezsPV2IgtLKRn99IiKiumDY8WPnL/TqGAO00Kga/6uMNBrQOjIYQgC/8tYRRETkoxh2/Nj5C70poTL06rjc2CocALD5Dw5lERGRb2LY8WPnS20AgNDAxp+v43Jjq6qhrM1/nJOtBiIiosth2PFjf/bsyBd2bmgZBkkCjuYWI9daLlsdREREtWHY8WMFrp6dIPmGscyBOnSKMQEArr99LLQ63RW3hJatZKuXiIiaHo3cBVD92J1OWMvkH8YCqubt7MkqRElwDGav2HvF9s+O6NQIVREREVVhz46fKiy1QQDQqVUI1DXePbFqknRhkrI+tguEELLWQkREdCn27Pgp1+Rkc6AWkiRVO253OKHVXb7Hx263e6WW6xPDoFVLgCkS1nI7TAHyDasRERFdimHHT11xcrLTidmr9l/2HFMHt/NKLYE6Dbq0MCP9xHlkFZQx7BARkU/hMJaf8oVr7Fysd0IYACDrfJnMlRAREXli2PFTf67EkndyskufxAthp4Bhh4iIfEu9wk7Lli1x7lz1i8gVFBSgZcuW11wUXZnrVhFyr8Ry6ZkQCiGcKCyzoaTCO3OBiIiIvKFeYef48eNwOBzV9ldUVCArK+uai6LLK6t0oNzuBFA1QbmhuSY7X24LNwbBlpsBgL07RETkW65qgvKyZcvcf//xxx9hMpncjx0OB9LS0pCQkOC14hwOB1555RV8/vnnyM7ORkxMDB588EG89NJL7hVIQgi8/PLL+Oc//4mCggLcdNNNmD9/Ptq0aeO1OnxNQVlVr06wXgOtuhFGIusw2RkA/v7mO9BFtUJWQRmuiwpp+LqIiIjq4KrCzp133gkAkCQJY8eO9Tim1WqRkJCAd955x2vFvfnmm5g/fz4++eQTdOzYEdu3b8e4ceNgMpnwxBNPAABmz56NOXPm4JNPPkFiYiKmT5+OIUOGYP/+/TAYDF6rxZcUupad+9iqp4rMfUCv23GaPTtERORDrirsOJ1VQyeJiYnYtm0bmjVr1iBFuWzevBl33HEHRowYAQBISEjAl19+id9++w1AVa9OamoqXnrpJdxxxx0AgE8//RRRUVFYunQpRo0a1aD1yaXwwpWTjT4WdspPVfX+nC2uRLnNAYNW3osdEhERAfWcs5ORkdHgQQcAbrzxRqSlpeHw4cMAgF27dmHTpk0YNmyYu47s7GwkJye7n2MymdCnTx9s2bKl1vNWVFTAarV6bP7EFXZMPrLs3MVZWuBeCs/eHSIi8hX1vqhgWloa0tLSkJub6+7xcfn444+vuTAAeP7552G1WtGuXTuo1Wo4HA689tprGD16NAAgOzsbABAVFeXxvKioKPexmsyaNQt/+9vfvFKjHFxhx9eGsQCguTkA50ttOF1QjpYRwXKXQ0REVL+enb/97W8YPHgw0tLScPbsWZw/f95j85ZvvvkG//nPf/DFF19gx44d+OSTT/D222/jk08+uabzTps2DYWFhe4tMzPTSxU3Dl8dxgKAGHMAAK7IIiIi31Gvnp0FCxZg0aJFGDNmjLfr8fDMM8/g+eefd8+96dy5M06cOIFZs2Zh7NixsFgsAICcnBxER0e7n5eTk4Nu3brVel69Xg+9Xt+gtTcUSaNDSWXVsn9f7dkBgNyictgczsZZLUZERHQZ9fpNVFlZiRtvvNHbtVRTWloKlcqzRLVa7TFR2mKxIC0tzX3carVi69atSEpKavD65KA2VQ3Z6TQq6DW+FySMAVqEGDRwCuBMYbnc5RAREdUv7Dz88MP44osvvF1LNbfddhtee+01rFixAsePH8eSJUvw7rvv4q677gJQtQR+ypQpePXVV7Fs2TLs2bMHDzzwAGJiYtzL5JVGY6rqzTIH1Hy3c1/AoSwiIvIl9RrGKi8vx0cffYQ1a9agS5cu0Go9h1PeffddrxQ3d+5cTJ8+HY8//jhyc3MRExODRx55BDNmzHC3efbZZ1FSUoKJEyeioKAAffv2xapVqxR7jR1Xz44vztdxaW4OwKHsIpzmTUGJiMgH1Cvs7N692z0nZu/evR7HvNnbEBISgtTUVKSmptbaRpIkzJw5EzNnzvTa6/oyjbmqZ8fk42EHAM5Yy+FwCqhVvtkDRURETUO9ws66deu8XQfVkeZCz44vTk52CQ3UIkCrRpnNgRxruXtYi4iISA6+N8OVLkt9oWfHl4exJElCjLlqGJHzdoiISG716tkZOHDgZYer1q5dW++CqHYOp4DG6Ps9O0DVUNYfeSW8kjIREcmuXmHn0mvY2Gw27Ny5E3v37q12g1DynhxrOSSNFiqp6o7nviz6wtBVdmE5hBA+u3KMiIiUr16/Md97770a97/yyisoLi6+poKodifOlQIAQgxaqHx80m9EsB4alYRyuxPnS20IC9LJXRIRETVRXp2zc//993vtvlhUXWZ+Vdjx9SEsAFCrJEQZq+btnCnkUBYREcnHq2Fny5Ytir2+jS/IPF8Vdnx5cvLFLCZX2OGVlImISD71Gsa6++67PR4LIXDmzBls374d06dP90phVN2pCxfpMxp8e76OS4zJgHQw7BARkbzq9VvTZDJ5PFapVGjbti1mzpyJwYMHe6Uwqi7rQtgJMfhXz05+SSUqbA7otWqZKyIioqaoXmFn4cKF3q6D6uCUexjLP3p2AnUamAK0KCyz4Yy1HAnhQXKXRERETdA1/dZMT0/HgQMHAAAdO3ZE9+7dvVIUVWdzOJFtrRoOMvpJzw5QNZRVWGbDmQKGHSIikke9wk5ubi5GjRqF9evXw2w2AwAKCgowcOBAfPXVV4iIiPBmjYSq69U4BSDslQjU+c9wULQpAAeyi7gii4iIZFOv1ViTJ09GUVER9u3bh/z8fOTn52Pv3r2wWq144oknvF0j4c/JyY6is351gb7oC7eNyLaWwymEzNUQEVFTVK+enVWrVmHNmjVo3769e1+HDh0wb948TlBuIK75OvbCXJkruTphQTro1CpUOpw4V1yJiBC93CUREVETU6+eHafTCa22+rwRrVYLp9N5zUVRda4bajqK8mSu5OqoJOmi6+1wKIuIiBpfvcLOLbfcgieffBKnT59278vKysJTTz2FQYMGea04+pNr2bm/9ewAQDQvLkhERDKqV9j54IMPYLVakZCQgFatWqFVq1ZITEyE1WrF3LlzvV0j4eI5O/7VswMw7BARkbzqNWcnNjYWO3bswJo1a3Dw4EEAQPv27ZGcnOzV4uhP7mEsP+zZsVy4R1ZhmQ2llXaZqyEioqbmqnp21q5diw4dOsBqtUKSJPzlL3/B5MmTMXnyZPTu3RsdO3bEzz//3FC1NlkOp8BpV9ix+l/Y0WvVCL9w13P27hARUWO7qrCTmpqKCRMmwGg0VjtmMpnwyCOP4N133/VacVQlt6gcdqeARiXBUXJe7nLqhUNZREQkl6sKO7t27cLQoUNrPT548GCkp6dfc1HkyTVfJ9psAIR/rnaLNgUAAM4UcEUWERE1rqsKOzk5OTUuOXfRaDTIy/O/CbS+zrUSq4U5UOZK6s91ccGcogpA5R/39iIiImW4qrDTvHlz7N27t9bju3fvRnR09DUXRZ5cFxRsHhogcyX1Zw7QwqBVweEU0EYmyl0OERE1IVcVdoYPH47p06ejvLz6vIuysjK8/PLLuPXWW71WHFVxrcRq4cdhR5Ik91CWLrqtzNUQEVFTclXjCS+99BK+++47XHfddUhJSUHbtlW/tA4ePIh58+bB4XDgxRdfbJBCmzLXnJ3mZv8NO0DVJOWMsyXQxbSTuxQiImpCrirsREVFYfPmzXjssccwbdo0iAs3dpQkCUOGDMG8efMQFRXVIIU2Ze45O6H+O2cH+HNFli6GPTtERNR4rnqmaHx8PH744QecP38eR48ehRACbdq0QWhoaEPU1+Q5nQKnFDCMBQBRRgMkCdCENMPpgjLE+HlPFRER+Yd6L4sJDQ1F7969vVkL1eBsSQUq7U6oJLhvqOmvtGoVIoL1yC2qQPqJ8ww7RETUKOp1byxqPK75OhajAVq1/39drqGsHSf98+KIRETkf/z/t6fCuebr+POy84u5VmTtOMGwQ0REjYNhx8edUsjkZBdXz86+01aU2xwyV0NERE0Bw46Pyyq4cEFBhcxvCTFo4Cg+B7tTYPepQrnLISKiJoBhx8f92bOjjLAjSRIqTx8CAGw/kS9zNURE1BQw7Pg4pc3ZAeAOOztOFMhbCBERNQkMOz5MCKG4OTsAUHH6AICqFVmuC1MSERE1FIYdH3a+1IayC5N4o/38GjsXs+VmQKdRIb+kEhlnS+Quh4iIFI5hx4e5hrAiQ/QwaNUyV+NFTju6tjABANK5BJ2IiBoYw44PO3X+wkosBc3XcekRX3V7EYYdIiJqaD4fdrKysnD//fcjPDwcAQEB6Ny5M7Zv3+4+LoTAjBkzEB0djYCAACQnJ+PIkSMyVuw9WQXKm6/j0is+DADDDhERNTyfDjvnz5/HTTfdBK1Wi5UrV2L//v145513PG46Onv2bMyZMwcLFizA1q1bERQUhCFDhqC8vFzGyr3DNTlZKdfYuViPODMA4EhuMQpLbfIWQ0REilbvG4E2hjfffBOxsbFYuHChe19iYqL770IIpKam4qWXXsIdd9wBAPj0008RFRWFpUuXYtSoUY1eszcp7Ro7FwsP1iOxWRAyzpZgx8nzGNguUu6SiIhIoXy6Z2fZsmXo1asX/u///g+RkZHo3r07/vnPf7qPZ2RkIDs7G8nJye59JpMJffr0wZYtW2o9b0VFBaxWq8fmi5Q8ZwcAesRx3g4RETU8nw47x44dw/z589GmTRv8+OOPeOyxx/DEE0/gk08+AQBkZ2cDAKKiojyeFxUV5T5Wk1mzZsFkMrm32NjYhnsT18A1ZydWoWGnVwLDDhERNTyfDjtOpxM9evTA66+/ju7du2PixImYMGECFixYcE3nnTZtGgoLC91bZmamlyr2nsIyG4rK7QCAGAXO2QGAnhdWZO3MLIDd4ZS5GiIiUiqfDjvR0dHo0KGDx7727dvj5MmTAACLxQIAyMnJ8WiTk5PjPlYTvV4Po9Hosfka1zV2woJ0CNT59NSqemsdEYwQgwZlNgcOnCmSuxwiIlIonw47N910Ew4dOuSx7/Dhw4iPjwdQNVnZYrEgLS3NfdxqtWLr1q1ISkpq1Fq9zTWEpcSVWC4qlXTRvB3eFJSIiBqGT4edp556Cr/++itef/11HD16FF988QU++ugjTJo0CUDVHbSnTJmCV199FcuWLcOePXvwwAMPICYmBnfeeae8xV+jrAuTk5W4EutivVwXFzxZIG8hRESkWD4ddnr37o0lS5bgyy+/RKdOnfD3v/8dqampGD16tLvNs88+i8mTJ2PixIno3bs3iouLsWrVKhgM/n0vKSVfY8fucEKr00Gr0+H5iVWXB1jy8y73PteW0LKVzJUSEZES+PxkkFtvvRW33nprrcclScLMmTMxc+bMRqyq4bmHsZTYs+N0Yvaq/QCASrsTCzb8AY0xAtP/+ztCDFp3s2dHdJKrQiIiUhCf7tlpyprCnB0A0GlUaBaiBwCcKfT/q14TEZHvYdjxUa7VWIrs2blEjKlqyJFhh4iIGgLDjg8qq3TgXEklAGXeBPRS0aaqQHf6Qm8WERGRNzHs+CDXEFaIXgNTgPYKrf1f9IWenbziCth4cUEiIvIyhh0fpPR7Yl0qxKBBsF4DITiURURE3sew44OayuRkF0mS3O81i0NZRETkZQw7PqgpTU52iTFXDWVx3g4REXkbw44Pamo9O8Cf7zW7sBwOp5C5GiIiUhKGHR/k6tlpCiuxXMKCdDBoVbA7BXKLOG+HiIi8h2HHB51qgsNYnLdDREQNhWHHx1Tanci50LPRlIaxACDGFXbOM+wQEZH3MOz4mOzCcggB6DUqNAvWyV1Oo3KFu9OF5XAKztshIiLvYNjxMacKLlxjxxwASZJkrqZxRQTroVVLqLQ7ca64Uu5yiIhIIRh2fExTXHbuolJJiLlw6wjXhRWJiIiuFcOOjznlXonV9MIO8Of7PsV5O0RE5CUMOz6mKV5j52Ku5fZZBWWAxP88iYjo2vG3iY9pysNYABAZoodOrUKF3QltRILc5RARkQIw7PiYP3t2ms4FBS+mUknuW0foYzvLXA0RESkBw44PcTgFzhQ27Tk7ABAbVhX09LGdZK6EiIiUgGHHh+QWlcPmENCoJEQZDXKXIxtX0NM17wC7wylzNURE5O8YdnyIa76OxWSAWtW0rrFzsYhgPfQaFVT6QOzJKpS7HCIi8nMMOz6kqa/EcpEkyd27s/mPczJXQ0RE/o5hx4c0xRuA1ib2whL0TUfOylwJERH5O4YdH+Lq2XFda6Ypiwuv+gy2n8hHaaVd5mqIiMifMez4EPfVk5v4MBYAmAO0sBfmwOYQ2HosX+5yiIjIjzHs+JCsC/eD4jBW1bydihM7AQAbDufJWwwREfk1hh0fIYTgBOVLlB/fCQD4+QjDDhER1R/Djo/IL6lEua3qmjLR5qZ7jZ2LVWTuhkoC/sgrcQdBIiKiq8Ww4yNcv8yjjHroNWqZq/ENoqIU3WLNAIBN7N0hIqJ6YtjxEe5l5xzC8tDvuggAwMbDXIJORET1w7DjI/682zmXnV/MHXaO5MHGW0cQEVE9MOz4iFMXVmI15RuA1qRrCzPCg3QoKrdj23EuQScioqvHsOMjTuZXhZ24MPbsXEytkjCwXSQAIO1ArszVEBGRP2LY8RGZF4axYjmMVU1ye1fYyYEQQuZqiIjI3zDs+AAhBDLZs1Orm9tEQKdW4fi5UvyRVyJ3OURE5GcYdnxAXlEFKuxOqCReY6cmQXoNbmgVDqCqd4eIiOhqMOz4ANd8nRhzALRqfiU1+XMoi/N2iIjo6vA3qw/IvLASi/N1anfLhUnK20/kI7+kUuZqiIjIn/hV2HnjjTcgSRKmTJni3ldeXo5JkyYhPDwcwcHBGDlyJHJy/Guo4+S5qsnJnK9TuxahgegYY4RTAD/uy5a7HCIi8iN+E3a2bduGDz/8EF26dPHY/9RTT+F///sfFi9ejA0bNuD06dO4++67ZaqyftzLzsMZdi5nRJdoAMAPe87IXAkREfkTvwg7xcXFGD16NP75z38iNDTUvb+wsBD//ve/8e677+KWW25Bz549sXDhQmzevBm//vqrjBVfnUxeULBORnSuCjub/zjHoSwiIqozvwg7kyZNwogRI5CcnOyxPz09HTabzWN/u3btEBcXhy1bttR6voqKClitVo9NTlx2Xjfx4UHoGGOEwyk4lEVERHXm82Hnq6++wo4dOzBr1qxqx7Kzs6HT6WA2mz32R0VFITu79l+Gs2bNgslkcm+xsbHeLrvOKuwOZFvLAQCxDDse7A4ntDqdx7b563kAgKfe/QxanQ4JLVvJXCUREfk6jdwFXE5mZiaefPJJrF69GgaD964/M23aNEydOtX92Gq1yhZ4ss6XQQggUKdGeJBOlhp8ltOJ2av2e+wqKK3EJ1tOICCxG/62ZBdevqurTMUREZG/8OmenfT0dOTm5qJHjx7QaDTQaDTYsGED5syZA41Gg6ioKFRWVqKgoMDjeTk5ObBYLLWeV6/Xw2g0emxyufieWJIkyVaHvzAH6hARoocQwJHcIrnLISIiP+DTPTuDBg3Cnj17PPaNGzcO7dq1w3PPPYfY2FhotVqkpaVh5MiRAIBDhw7h5MmTSEpKkqPkq+a6J1YLXmOnztpFhSCvqAIHzjDsEBHRlfl02AkJCUGnTp089gUFBSE8PNy9f/z48Zg6dSrCwsJgNBoxefJkJCUl4YYbbpCj5KvGyclXr60lBJv+OItsazk0oTFyl0NERD7Op8NOXbz33ntQqVQYOXIkKioqMGTIEPzjH/+Qu6w6c4Wd2DAuO6+rIL0G8WGBOH6uFIEdBspdDhER+Ti/Czvr16/3eGwwGDBv3jzMmzdPnoKu0Ylz7Nmpjw7RRhw/V4qADgPgcAqoVZzvRERENfPpCcpKJ4TAiXMlAKquIUN1l9gsCHqNCpqQZtjyxzm5yyEiIh/GsCOjvOIKlFQ6oJI4jHW1NGoVrosKAQB8vT1T5mqIiMiXMezI6PjZqiGsGHMA9Bq1zNX4n07Nqy4ZsGrvGeQWlctcDRER+SqGHRkdvzCEldiMQ1j1ERliQMXpg7A5BL76jb07RERUM4YdGR0/WxV2Ejhfp95Kdv4AAPhi60nYHU6ZqyEiIl/EsCOj4+7JyVyJVV9lR7agWbAO2dZyrN6fI3c5RETkgxh2ZOSas8NhrGvgsGNU7zgAwKLNx+WthYiIfBLDjkyEEBf17DDsXIvRN8RBo5KwNSMfOzML5C6HiIh8DMOOTPKKK1DKZedeEW0KwJ3dmwMA5q8/KnM1RETkaxh2ZMJl5971aP+WAIAf9+XgKO+GTkREF2HYkYlrJRbn63hH68gQDO4QBQD4cMMxmashIiJfwrAjE9d8HS47957HBrQCACz5Pct9g1UiIiKGHZlw2bn3dY8LRd/WzWB3CqSuOSJ3OURE5CMYdmSSwWXnDeL/DWkLAFjy+ykcyeHcHSIiYtiRBe923nC6xZoxpGMUnAJ456fDcpdDREQ+gGFHBjlWLjtvSE8PbgtJAlbty+Z1d4iIiGFHDkdziwFU9epw2bn3XRcVgru7twAAzPzfPgghZK6IiIjkxLAjA9d1YFpFBMtciXI9M6QtAnVq7DhZgKU7s+Quh4iIZMSwI4OjeVU9O60jGXYaisVkwKSBrQEAs344iOIKu8wVERGRXBh2ZOAaxmLYaVjj+yYiPjwQuUUVmLuWS9GJiJoqhh0ZHM2tWonFsNOwDFo1ZtzaAQDwr58zsP+0VeaKiIhIDgw7jayw1IazxRUAgFYRXHbe0Aa1j8LwzhY4nALPf7cbdodT7pKIiKiRMew0sqN5VZOTLUYDQgxamatpGl65vSOMBg12nyrEwl+Oy10OERE1MoadRlbbfJ2Elq2g1emuuNntnGh7tSJDDHhxRHsAwNs/HXJ/B0RE1DRo5C6gqakt7GSdysTsFXuv+Pypg9s1SF1K99desVi++wx+PnIWT329E989fiO0amZ9IqKmgP/aNzJX2GnFycmNSpIkvHVPV5gCtNiTVYi5aVydRUTUVDDsNDL3NXZ4QcFGZzEZ8NpdnQAAH6w7it8y8mWuiIiIGgPDTiMqtzlw6nwZAC47l8utXWJwd/fmcArgiS9/R35JpdwlERFRA2PYaUR/5BVDCMAUoEWzYJ3c5TQpF08A/+ChfrDln0K2tRwdHnoDWp3efSyhZSu5SyUiIi/jBOVG5Jqv0yYyGJIkyVxN03LpBPC8ogp8vT0ThsSeGPnBevRJDAcAPDuik1wlEhFRA2HPTiPaf6bqCr7tokNkroQiQvQY2DYCAPDrsXwuRyciUjCGnUbkul1B+2ijzJUQAHSMMaFrCxMA4Kf92e4rWxMRkbIw7DSiA2eqrp7MsOM7+rWJQGxoAGwOgf/tOg1VAL8bIiKlYdhpJHlFFThbXAFJAtpZOIzlK1QqCcM7R8MUoIW13I6w255FpZ33zyIiUhKGnUZy4MJ8nYTwIATqOC/clxi0atzWJRo6tQr6Fh3x8rJ9EELIXRYREXkJw04jcYWdDhzC8knhwXoM7WSBEE58+dtJzEk7KndJRETkJQw7jcQVdtpzJZbPSmwWhMK1/wIAvLfmMD7bclzegoiIyCsYdhoJJyf7h5JdKzEluQ0AYMayfVi267TMFRER0bXy6bAza9Ys9O7dGyEhIYiMjMSdd96JQ4cOebQpLy/HpEmTEB4ejuDgYIwcORI5OTkyVVyzCrsDf1y4JxbDjnfZHU731Y8vt9nt9jqf88lBbTA2KR5CAE9/sxMbD+c14DsgIqKG5tMzZTds2IBJkyahd+/esNvteOGFFzB48GDs378fQUFBAICnnnoKK1aswOLFi2EymZCSkoK7774bv/zyi8zV/+lITjHsTgFTgBbRJoPc5SiL04nZq/ZfsdnUwe3qfEpJkvDybR1xvtSGZbtO45HP0vH5w9ejZ3zYtVRKREQy8emws2rVKo/HixYtQmRkJNLT09GvXz8UFhbi3//+N7744gvccsstAICFCxeiffv2+PXXX3HDDTfIUXY1F8/X4W0i/INKJeHt/+uKgjIbNh7OwwP//g0fP9gbfVqGy10aERFdJZ8exrpUYWEhACAsrOr/sNPT02Gz2ZCcnOxu065dO8TFxWHLli21nqeiogJWq9Vja0j7z/DKyf5Ip1Hhw/t7om/rZiipdODBhduw+ehZucsiIqKr5Ddhx+l0YsqUKbjpppvQqVPVzRqzs7Oh0+lgNps92kZFRSE7O7vWc82aNQsmk8m9xcbGNmTp2JVZAADocuHWBOS7Lp0DZAwOwNdPDEJ5xg6U2Ry4b8FGBLfpDa3eUKe5QryLOhGR/Hx6GOtikyZNwt69e7Fp06ZrPte0adMwdepU92Or1dpggafS7sTeC/fE6hYb2iCvQV5Uyxwgu9OJH/ZkI+MsEHXP33Dmm1fw2oLPr3g63kWdiEh+ftGzk5KSguXLl2PdunVo0aKFe7/FYkFlZSUKCgo82ufk5MBisdR6Pr1eD6PR6LE1lANnrKi0O2EO1CIhPLDBXocalkalwojO0WgVEQSHEIi4+0X3XCwiIvJtPh12hBBISUnBkiVLsHbtWiQmJnoc79mzJ7RaLdLS0tz7Dh06hJMnTyIpKamxy63RzgtDWN1izZyc7OfUKgnDOkWjbVQIJLUGP+3PwW8Z+by1BBGRj/PpYaxJkybhiy++wPfff4+QkBD3PByTyYSAgACYTCaMHz8eU6dORVhYGIxGIyZPnoykpCSfWYl1cdgh/6dWSRjSMQq/fb8QphvuwZZj51BUbsPAtpFQqRhmiYh8kU/37MyfPx+FhYUYMGAAoqOj3dvXX3/tbvPee+/h1ltvxciRI9GvXz9YLBZ89913MlbtacnG3wEALz0+xmsXvSN5SZKEgg2fYMB1EQCAvaet+N/u07xbOhGRj/Lpnp26DA8YDAbMmzcP8+bNa4SKrk5BaSUQEgkAeDF1EQxada1tr+aid+QbusaaEWzQYOXebBw/V4rF6Zm4tUsMTAFauUsjIqKL+HTPjr9zDWGZA7SXDTrkv1pFBGNkj+YI1KlxtrgSX/12EifzS+Uui4iILsKw04B+P1kAALDwFhGKFm0KwKjesYgy6lFud2Lp71lIP3GeE5eJiHwEw04DcvXsWIwMO0oXYtDinh4t0CHaCAFg09GzWLUvG5KW3z0RkdwYdhqIEAK7ThUAYM9OU6FRq5DcPhIDrouASgIO5xQj8v53sDerUO7SiIiaNIadBrTk8ZuQvzIVzYL1cpdCjUSSJHSNNePuHi0QrNdAExqDu/+xGQt/yeCwFhGRTBh2GogkSUhsFoSyAxug5vVXmpzm5gCM7hOHsqNbUelw4m//248Jn27H+ZJKuUsjImpyGHaIGohBq0b+sjfwt9s7QqdWYc2BXAxO3Yi0Azlyl0ZE1KQw7BA1sLE3JmDJpBvROjIYeUUVGP/JdjyzeBes5Ta5SyMiahIYdogaQccYE5ZP7ouJ/VpCkoDF6acw9L2N+PlIntylEREpHsMOUSMxaNV4YXh7fPNIEuLCAnG6sBxj/v0b/t/iXZzLQ0TUgBh2iBpZ74QwrHzyZjyQFA9JAr5NP4VB727Akt9PccUWEVEDYNghkkGQXoOZd3TCt48mAYWnkV9Siae+3oXo+16HISKuxpvFJrRsJXfZRER+yadvBEqkdD3jw5C18EncM3ctfsvIhyGhG5o/vAA94s3onRAGrfrP/x95dkQnGSslIvJfDDtEDcjucEKr012+jd2O6xPC0CYyGOsP5eFkfim2HT+PA2eKcHObZmgTGQxJ4rWaiIjqi2GHqCE5nZi9av9lm0wd3A4AEBqow53dYnDsbAk2Hs6DtdyOlXuzscccgP5tIxqjWiIiRWLYIfIhkiShVUQw4sMCkX7iPLadOI9TBWX44reTMA0cj8JSG0yBWrnLJCLyK5ygTOSDNGoV+rQMxwM3xKN1RDCEAIK734oBb6/Dp1uOw+5wyl0iEZHfYNgh8mHGAC1GdInGXd2bw3b2JM6X2jDj+30Y9v7P2HCYFyQkIqoLhh0iPxAXFojcz57C3+/shNBALY7kFmPsx79h3MLfcDS3WO7yiIh8GsMOkb8QToy5IR7r/99APNw3ERqVhHWH8jA0dSP+9r99KCjlVZiJiGrCsEPkZ0yBWrx0awf89FQ/JLePgt0psPCX4xjw9np8svk4bJzPQ0TkgauxiPxEbdfs0cd1hWnAOBQ0i8fLy/bh5c/SsGjKbRjQNlKGKomIfA/DDpG/uMw1e5xOgb2nC/HrsXyUmSx4cOE2DGgbgZdGtEfryJBGLpSIyLdwGItIAVQqCV1amDE2KR5F25dCq5aw/lAehqT+jFeWcT4PETVtDDtECqLXqmHd+Al+eqo//tIhCg6nwKLNx9H/rfVY9EsG5/MQUZPEsEOkMHaHE9fFhGLRwzfi7OIZsOUdR2GZDa/8bz8SHv0ngq+7gXdRJ6ImhXN2iJTmkrk9TiGwL8uKLcfOAeEt0Ozu6YgPD8Rv74yXsUgiosbDnh0ihVNJEjq3MGHsjfHoEWeGSgJOnCtF5AOpePn7vThfwvk8RKRsDDtETYReo8bNbSIw5oZ4tIoIgqRS45MtJ9D/rXX4eBPn8xCRcjHsEDUx5kAdbu0Sg7zFM9DOEgJruR0zl+/HkNSNWHswB0IIuUskIvIqhh2iJqr0+C6kPTcY51f/A47SQhzLK8FDi7aj+YPvIaTdTdDq9IqZyJzQshW0Ot1lNyW8TyKqGScoEzVVTidmL98NAKiwO7At4zx2niqALrotwu98AeFBOvRKCMXHD/eVudBrl3UqE7NX7L1sm2dHdGqkaoiosbFnh4ig16jRt00zjLsxAb3iQ6FTq3CupBI/7stB1PgFSF1zGKcLyuQuk4ioXtizQ0RuQXoNbmrdDL3iQ7ErqxA7TxagzBiB1DVHMCftCAa0jcSo3rHo3zYCeo1a7nKR0LIVsk5lVtuvCg6DxhQFtTESKkMIgm64F5uOnIWAgEqSoNOoEKBVI8SggTFAC1OAVobqiaixMOwQUTV6rRrXJ4ShR6wZf3/mcQyf/Bp+PZaPtQdzsfZgLoL1GvS/LgJ/6RCFgW0jYQqUJyxkncrEi9/+jlxrOXKKKpBrLUduUQUq7NVXlqWfPF/rebRqCc3+byZm/XAAXWPN6BEXCovJ0JClE1EjYtgholpp1CqUHdqEryYm4VheMb7eloklv2cht6gCK/acwYo9Z6BRSbDnHEXR8d2oPHMIlacPwVlaUOP5mreIxfFjf9SrFodTIDO/FPvPWLH7VCH2ZhUi+vFPsWjz8WptJQkI0Vf12gTq1Ehf+TX63zkaEiQ4hECF3YGySgeKyu0oLLPB5hDQx3bGhxuPuc9hO3cKFSd3oeLkblRk7oWoLL3m90BE8mDYIaI6aRkRjGnD2+O5oe2wO6sQq/dnY/X+HBzOKQYiWiEk4s/VTCEGDcKCdDAHaGEO1MEUoEWIQYO3xw9GSYUdgTo1JEmq9hpllQ7kWMurtqIKZBeW4XBOMQ7nFOFwThHKbZ49NipDCNSShPBgHSKNekSFGBBp1CM8SA+16s/zr566ADc/M6XG9+UUAvkllXj/5acxYMJ05FgrcLaoAtrwFtCGt0Bw9xGQJCAqxIC4sECsePMxVNqd0Gk45ZHIXzDsENFVUakkdIs1o1usGc8MaYeT50rRedBduPmhl3DGWo5zxZUoKrejqNyOE5c8N3riv9Dx5R+hUUkINmggRNW9vOxOAbtTwOG8/DV+9BoVrosKQecWJnRubsL4uwdj5r+/9wg2V/1+JAnNgvUo2bMGg9p9AACosDmQeb4MmfmlOHm+FAWlNmRby5FtLUfEva+h699+Qp+WYejbuhlubNUM10UFQ6Nm+CHyVYoJO/PmzcNbb72F7OxsdO3aFXPnzsX1118vd1lEihcXHojS/eswqP1cAFXL2POKKlBQakNBmQ0FpZUoKLOhtMKB0vIKSGoN7E6BglJbjecL0KphMRkQGaJHpNGAls2C0M4SgraWEMSHB3kEmwdyj11T0KmNXqtG68hgtI4MBgBYy21VwSe/FAcyslAWaML6Q3lYfygPAKDTqNA6IhjtokPQzhKChPAgRBqr3kOzYD17gYhkpoiw8/XXX2Pq1KlYsGAB+vTpg9TUVAwZMgSHDh1CZGSk3OURNSl6jRotQgPRIrT6sWdHdIK1uAzWchuKym0AJGjVEjRqFTQqCQE6NUL0mhqHuORkNGjRMcaEjjEmrHtuMHafyMMvR8/i5yNnsf14PkoqHdh/xor9Z6w1Pt8cqEWgVg2DVg29Vg29RgWdWgVJqupZUqku/ClJUF3YJ130d5UKFx5ffBy1t5cuaa/yfG5Nx9UqCQFaddWmq/ozUKeGQadGsF4Dc4AWxgAtDFr5V+ERXS1FhJ13330XEyZMwLhx4wAACxYswIoVK/Dxxx/j+eefl7k6Iv9mdzih1eku38Zur/O5jMEBV24oqQBx5Xt11fV1vUugfbQR7aONePjmlnA6BU6dL8OBbCsOZRfhYLYVWefLsOvwcQh9CCS1pqqXCzX3ZPkbp60CorwIzooSOMuL4SwvRoBa4OEx98EcqIUpsGqulilAC3OgFuYAHUyBWoToNVA1QC8cyavGyz9IKkhqLaDRQpLUgARExzTH0T07ZBvu9fuwU1lZifT0dEybNs29T6VSITk5GVu2bJGxMiKFcDoxe9X+yzaZOrid187lOt+7Px2sUzu5qVQS4sIDERceiCEdLe79Wt0teHP5HpTbnCittFfNS3II2J1OOC7MTxIAPn19Ku6f9g4gAAFACEBAXPgTEKKq3Xcf/B13TZrufnxpOwhg5SdzMOSBybWeQ4iqdhu//wx9b7/f45hTVNVnczir/nQ6kfnHYTSLbYVKhxMVNicEAJVWD2j1UIc08/gc/rUp4/KfkwQYA7TuIBSgUyNQp6nWk6TXqqFRSdCoJWhUEtQq1YU/q3oB1SoV1CpAQlVwqqkT8OKeQcm976LjNTz30tO4Zo+5bhUn8Od8sj/3uR5Xn2t26fPcjy9qeunzxaUHanp+XV6jhkKqvZ+LCnGIquHnSrvTvVW4/u5wPXZ47KuwVf1pH/wCrotqDodDwCGq5t7Vdnu9k/mlaBkRXPPBBub3Yefs2bNwOByIiory2B8VFYWDB2v+x7KiogIVFRXux4WFhQAAq7XmLuhrIYRAeUlxXVrK0M6Xa6trO1+ura7tfLm2urbz5drq2q5u5xJC1OnfCiEEKkpLIAEIkgCoL2xuVb9eSw9sRELIlf9vt2jHcrQL+9tl23y1+Ut0e+b/XfFc36/5EEmTJl6x3QsvTsLkJekAqt5PpbPql1yFzYFyuxOVdgfKbU4s/+QDPPHM87CW2lBYXglrWdWSftdWbnPCCSC/HMiv/XJH5IdUAUYUWYuu2E4IJ6xFVlj1V+6xvRqun8Ur3sBY+LmsrCwBQGzevNlj/zPPPCOuv/76Gp/z8ssvX/j/G27cuHHjxo2bv2+ZmZmXzQp+37PTrFkzqNVq5OTkeOzPycmBxWKp8TnTpk3D1KlT3Y+dTify8/MRHh4OSZJgtVoRGxuLzMxMGI3GBq2f6o/fk3/g9+Qf+D35D35XfxJCoKioCDExMZdt5/dhR6fToWfPnkhLS8Odd94JoCq8pKWlISUlpcbn6PV66PV6j31ms7laO6PR2OT/Q/IH/J78A78n/8DvyX/wu6piMpmu2Mbvww4ATJ06FWPHjkWvXr1w/fXXIzU1FSUlJe7VWURERNR0KSLs3HvvvcjLy8OMGTOQnZ2Nbt26YdWqVdUmLRMREVHTo4iwAwApKSm1DltdLb1ej5dffrnaUBf5Fn5P/oHfk3/g9+Q/+F1dPUmIK63XIiIiIvJfvGELERERKRrDDhERESkaww4REREpGsMOERERKRrDTg3mzZuHhIQEGAwG9OnTB7/99pvcJfmtjRs34rbbbkNMTAwkScLSpUs9jgshMGPGDERHRyMgIADJyck4cuSIR5v8/HyMHj0aRqMRZrMZ48ePR3Gx5z2Mdu/ejZtvvhkGgwGxsbGYPXt2tVoWL16Mdu3awWAwoHPnzvjhhx+uuhYlmjVrFnr37o2QkBBERkbizjvvxKFDhzzalJeXY9KkSQgPD0dwcDBGjhxZ7arlJ0+exIgRIxAYGIjIyEg888wz1e5Kvn79evTo0QN6vR6tW7fGokWLqtVzpZ+/utSiRPPnz0eXLl3cF5JLSkrCypUr3cf5HfmmN954A5IkYcqUKe59/K5k4I37UynJV199JXQ6nfj444/Fvn37xIQJE4TZbBY5OTlyl+aXfvjhB/Hiiy+K7777TgAQS5Ys8Tj+xhtvCJPJJJYuXSp27dolbr/9dpGYmCjKysrcbYYOHSq6du0qfv31V/Hzzz+L1q1bi/vuu899vLCwUERFRYnRo0eLvXv3ii+//FIEBASIDz/80N3ml19+EWq1WsyePVvs379fvPTSS0Kr1Yo9e/ZcVS1KNGTIELFw4UKxd+9esXPnTjF8+HARFxcniouL3W0effRRERsbK9LS0sT27dvFDTfcIG688Ub3cbvdLjp16iSSk5PF77//Ln744QfRrFkzMW3aNHebY8eOicDAQDF16lSxf/9+MXfuXKFWq8WqVavcbery83elWpRq2bJlYsWKFeLw4cPi0KFD4oUXXhBarVbs3btXCMHvyBf99ttvIiEhQXTp0kU8+eST7v38rhofw84lrr/+ejFp0iT3Y4fDIWJiYsSsWbNkrEoZLg07TqdTWCwW8dZbb7n3FRQUCL1eL7788kshhBD79+8XAMS2bdvcbVauXCkkSRJZWVlCCCH+8Y9/iNDQUFFRUeFu89xzz4m2bdu6H//1r38VI0aM8KinT58+4pFHHqlzLU1Fbm6uACA2bNgghKj6HLRarVi8eLG7zYEDBwQAsWXLFiFEVahVqVQiOzvb3Wb+/PnCaDS6v5dnn31WdOzY0eO17r33XjFkyBD34yv9/NWllqYkNDRU/Otf/+J35IOKiopEmzZtxOrVq0X//v3dYYfflTw4jHWRyspKpKenIzk52b1PpVIhOTkZW7ZskbEyZcrIyEB2drbH520ymdCnTx/3571lyxaYzWb06tXL3SY5ORkqlQpbt251t+nXrx90Op27zZAhQ3Do0CGcP3/e3ebi13G1cb1OXWppKgoLCwEAYWFhAID09HTYbDaPz6Zdu3aIi4vz+J46d+7scdXyIUOGwGq1Yt++fe42l/sO6vLzV5damgKHw4GvvvoKJSUlSEpK4nfkgyZNmoQRI0ZU+zz5XclDMVdQ9oazZ8/C4XBUu81EVFQUDh48KFNVypWdnQ0ANX7ermPZ2dmIjIz0OK7RaBAWFubRJjExsdo5XMdCQ0ORnZ19xde5Ui1NgdPpxJQpU3DTTTehU6dOAKo+G51OV+1muZd+fjV9dq5jl2tjtVpRVlaG8+fPX/Hnry61KNmePXuQlJSE8vJyBAcHY8mSJejQoQN27tzJ78iHfPXVV9ixYwe2bdtW7Rh/nuTBsENEbpMmTcLevXuxadMmuUuhGrRt2xY7d+5EYWEhvv32W4wdOxYbNmyQuyy6SGZmJp588kmsXr0aBoNB7nLoAg5jXaRZs2ZQq9XVZqLn5OTAYrHIVJVyuT7Ty33eFosFubm5Hsftdjvy8/M92tR0jotfo7Y2Fx+/Ui1Kl5KSguXLl2PdunVo0aKFe7/FYkFlZSUKCgo82l/6+dX3OzAajQgICKjTz19dalEynU6H1q1bo2fPnpg1axa6du2K999/n9+RD0lPT0dubi569OgBjUYDjUaDDRs2YM6cOdBoNIiKiuJ3JQOGnYvodDr07NkTaWlp7n1OpxNpaWlISkqSsTJlSkxMhMVi8fi8rVYrtm7d6v68k5KSUFBQgPT0dHebtWvXwul0ok+fPu42GzduhM1mc7dZvXo12rZti9DQUHebi1/H1cb1OnWpRamEEEhJScGSJUuwdu3aakOCPXv2hFar9fhsDh06hJMnT3p8T3v27PEIpqtXr4bRaESHDh3cbS73HdTl568utTQlTqcTFRUV/I58yKBBg7Bnzx7s3LnTvfXq1QujR492/53flQzkniHta7766iuh1+vFokWLxP79+8XEiROF2Wz2mBVPdVdUVCR+//138fvvvwsA4t133xW///67OHHihBCiarm32WwW33//vdi9e7e44447alx63r17d7F161axadMm0aZNG4+l5wUFBSIqKkqMGTNG7N27V3z11VciMDCw2tJzjUYj3n77bXHgwAHx8ssv17j0/Eq1KNFjjz0mTCaTWL9+vThz5ox7Ky0tdbd59NFHRVxcnFi7dq3Yvn27SEpKEklJSe7jrqWygwcPFjt37hSrVq0SERERNS6VfeaZZ8SBAwfEvHnzalwqe6WfvyvVolTPP/+82LBhg8jIyBC7d+8Wzz//vJAkSfz0009CCH5Hvuzi1VhC8LuSA8NODebOnSvi4uKETqcT119/vfj111/lLslvrVu3TgCoto0dO1YIUbXke/r06SIqKkro9XoxaNAgcejQIY9znDt3Ttx3330iODhYGI1GMW7cOFFUVOTRZteuXaJv375Cr9eL5s2bizfeeKNaLd9884247rrrhE6nEx07dhQrVqzwOF6XWpSopu8HgFi4cKG7TVlZmXj88cdFaGioCAwMFHfddZc4c+aMx3mOHz8uhg0bJgICAkSzZs3E008/LWw2m0ebdevWiW7dugmdTidatmzp8RouV/r5q0stSvTQQw+J+Ph4odPpREREhBg0aJA76AjB78iXXRp2+F01PkkIIeTpUyIiIiJqeJyzQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENERESKxrBDREREisawQ0RERIrGsENEipSQkIDU1FS5yyAiH8CwQ0Q+78EHH4QkSZAkyX0zzJkzZ8Jut9f6nG3btmHixImNWCUR+SqN3AUQEdXF0KFDsXDhQlRUVOCHH37ApEmToNVqMW3aNI92lZWV0Ol0iIiIkKlSIvI17NkhIr+g1+thsVgQHx+Pxx57DMnJyVi2bBkefPBB3HnnnXjttdcQExODtm3bAqg+jFVQUIBHHnkEUVFRMBgM6NSpE5YvX+4+vmnTJtx8880ICAhAbGwsnnjiCZSUlDT22ySiBsCeHSLySwEBATh37hwAIC0tDUajEatXr66xrdPpxLBhw1BUVITPP/8crVq1wv79+6FWqwEAf/zxB4YOHYpXX30VH3/8MfLy8pCSkoKUlBQsXLiw0d4TETUMhh0i8itCCKSlpeHHH3/E5MmTkZeXh6CgIPzrX/+CTqer8Tlr1qzBb7/9hgMHDuC6664DALRs2dJ9fNasWRg9ejSmTJkCAGjTpg3mzJmD/v37Y/78+TAYDA3+voio4XAYi4j8wvLlyxEcHAyDwYBhw4bh3nvvxSuvvAIA6Ny5c61BBwB27tyJFi1auIPOpXbt2oVFixYhODjYvQ0ZMgROpxMZGRkN8XaIqBGxZ4eI/MLAgQMxf/586HQ6xMTEQKP585+voKCgyz43ICDgsseLi4vxyCOP4Iknnqh2LC4urn4FE5HPYNghIr8QFBSE1q1b1+u5Xbp0walTp3D48OEae3d69OiB/fv31/v8ROTbOIxFRIrXv39/9OvXDyNHjsTq1auRkZGBlStXYtWqVQCA5557Dps3b0ZKSgp27tyJI0eO4Pvvv0dKSorMlRORNzDsEFGT8N///he9e/fGfffdhw4dOuDZZ5+Fw+EAUNXzs2HDBhw+fBg333wzunfvjhkzZiAmJkbmqonIGyQhhJC7CCIiIqKGwp4dIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJSNIYdIiIiUjSGHSIiIlI0hh0iIiJStP8fo1eH+r9idDQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "sns.histplot(df['Price'], kde=True)\n",
        "plt.title('Distribution of Price')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "iKDZnZHchirk",
        "outputId": "eb059aba-4a50-44e2-fce3-337ce4f76181"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(data['Price'], kde=True)\n",
        "plt.title('Distribution of Price')\n",
        "plt.xlabel('Price')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "ec1RBml8hirk",
        "outputId": "66151684-4f75-40e7-e09b-8b5544bf2c03"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='ram_gb', data=data)\n",
        "plt.title('Distribution of RAM')\n",
        "plt.xlabel('RAM (GB)')\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "NV9sM8K-hirl",
        "outputId": "c18d814f-f45c-4281-fada-722d8636c85d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(x='ram_gb', y='Price', data=data)\n",
        "plt.title('Price vs. RAM')\n",
        "plt.xlabel('RAM (GB)')\n",
        "plt.ylabel('Price')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dHvIlRc7hirm"
      },
      "source": [
        "**Model Building**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 113,
      "metadata": {
        "id": "lzM5sgB6hirm"
      },
      "outputs": [],
      "source": [
        "X = df.drop(columns=['Price'])\n",
        "y = df['Price']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "wIGFSoFIhirm"
      },
      "outputs": [],
      "source": [
        "gb_regressor = GradientBoostingRegressor()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "9-caEYyHhirm",
        "outputId": "4a192ca3-35b5-4a04-cfb4-17ffd91ac6a4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GradientBoostingRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingRegressor</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ],
      "source": [
        "gb_regressor.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MdeOmLachirn"
      },
      "source": [
        "**Model Evaluation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "id": "mVeXVXq-hirn"
      },
      "outputs": [],
      "source": [
        "y_pred_train = gb_regressor.predict(X_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "id": "IhC-TYWBhirn"
      },
      "outputs": [],
      "source": [
        "y_pred_test = gb_regressor.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_scores = cross_val_score(gb_regressor, X, y, cv=5, scoring='neg_mean_squared_error')\n",
        "print(\"Cross-Validation MSE:\", -cv_scores.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "OK-S1cornddh",
        "outputId": "738d8817-e753-4476-ba69-abfb42853adf"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation MSE: 1084536480.5838351\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Applying Random Forest**"
      ],
      "metadata": {
        "id": "kccuscJXnhyr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_regressor = RandomForestRegressor()\n",
        "rf_regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "sGVw_JXHnmxO",
        "outputId": "8708d140-f96e-421a-d673-608a3d6914fb"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train_rf = rf_regressor.predict(X_train)\n",
        "y_pred_test_rf = rf_regressor.predict(X_test)\n",
        "\n",
        "mse_train_rf = mean_squared_error(y_train, y_pred_train_rf)\n",
        "mse_test_rf = mean_squared_error(y_test, y_pred_test_rf)\n",
        "\n",
        "mae_train_rf = mean_absolute_error(y_train, y_pred_train_rf)\n",
        "mae_test_rf = mean_absolute_error(y_test, y_pred_test_rf)\n",
        "\n",
        "r2_train_rf = r2_score(y_train, y_pred_train_rf)\n",
        "r2_test_rf = r2_score(y_test, y_pred_test_rf)\n",
        "\n",
        "print(\"\\nRandom Forest Regressor:\")\n",
        "print(\"Mean Squared Error:\", mse_train_rf)\n",
        "print(\"Mean Absolute Error:\", mae_train_rf)\n",
        "print(\"R² Score:\", r2_train_rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "8ttbD130npdQ",
        "outputId": "a8cccc80-06cd-46d4-fb99-070426f1ab2c"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest Regressor:\n",
            "Mean Squared Error (Train): 109004799.99510047\n",
            "Mean Squared Error (Test): 847109644.6252016\n",
            "Mean Absolute Error (Train): 5153.513912993922\n",
            "Mean Absolute Error (Test): 14653.341007011675\n",
            "R² Score (Train): 0.9468854469048346\n",
            "R² Score (Test): 0.5653188474553668\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv_scores_rf = cross_val_score(rf_regressor, X, y, cv=5, scoring='neg_mean_squared_error')\n",
        "print(\"Cross-Validation MSE:\", -cv_scores_rf.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "F_NLkpVxnuR6",
        "outputId": "9f0d5e0c-9cc4-49fd-a617-201458c85934"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cross-Validation MSE: 1140308074.9998791\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "CXaPp2t7hiro"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "language_info": {
      "name": "python"
    },
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}