240916_과제_캐글(분류).ipynb

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  "metadata": {
    "kernelspec": {
      "language": "python",
      "display_name": "Python 3",
      "name": "python3"
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      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kaggle": {
      "accelerator": "none",
      "dataSources": [
        {
          "sourceId": 4471648,
          "sourceType": "datasetVersion",
          "datasetId": 2617198
        }
      ],
      "dockerImageVersionId": 30761,
      "isInternetEnabled": false,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": false
    },
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      "provenance": [],
      "include_colab_link": true
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ggoddll99/24-2ESAA/blob/main/240916_%EA%B3%BC%EC%A0%9C_%EC%BA%90%EA%B8%80(%EB%B6%84%EB%A5%98).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "source": [
        "\n",
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = 'drug200:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F2617198%2F4471648%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240920%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240920T053423Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fd79811-960a-47d2-f862-4b0d8e36ca88"
      },
      "cell_type": "code",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading drug200, 1716 bytes compressed\n",
            "\r[==================================================] 1716 bytes downloaded\n",
            "Downloaded and uncompressed: drug200\n",
            "Data source import complete.\n"
          ]
        }
      ],
      "execution_count": 1
    },
    {
      "cell_type": "code",
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "\n",
        "# Input data files are available in the read-only \"../input/\" directory\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "    for filename in filenames:\n",
        "        print(os.path.join(dirname, filename))\n",
        "\n",
        "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\"\n",
        "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
      ],
      "metadata": {
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        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "execution": {
          "iopub.status.busy": "2024-09-20T04:54:57.024436Z",
          "iopub.execute_input": "2024-09-20T04:54:57.025416Z",
          "iopub.status.idle": "2024-09-20T04:54:59.9738Z",
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        "id": "AGMdQUmu7U5H",
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        "outputId": "446c0bbd-5261-4fa7-aa14-f4d6e568d1c8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/kaggle/input/drug200/drug200.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Review"
      ],
      "metadata": {
        "id": "J4tPr8XH7U5K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv(\"/kaggle/input/drug200/drug200.csv\")"
      ],
      "metadata": {
        "execution": {
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        },
        "trusted": true,
        "id": "a3IEYOYs7U5Q"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "data"
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        "execution": {
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          "shell.execute_reply": "2024-09-20T04:55:00.039408Z"
        },
        "trusted": true,
        "id": "iRa0ikuC7U5T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "15a496d0-6dc6-4de0-f280-7c4b7329090a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age Sex      BP Cholesterol  Na_to_K   Drug\n",
              "0     23   F    HIGH        HIGH   25.355  drugY\n",
              "1     47   M     LOW        HIGH   13.093  drugC\n",
              "2     47   M     LOW        HIGH   10.114  drugC\n",
              "3     28   F  NORMAL        HIGH    7.798  drugX\n",
              "4     61   F     LOW        HIGH   18.043  drugY\n",
              "..   ...  ..     ...         ...      ...    ...\n",
              "195   56   F     LOW        HIGH   11.567  drugC\n",
              "196   16   M     LOW        HIGH   12.006  drugC\n",
              "197   52   M  NORMAL        HIGH    9.894  drugX\n",
              "198   23   M  NORMAL      NORMAL   14.020  drugX\n",
              "199   40   F     LOW      NORMAL   11.349  drugX\n",
              "\n",
              "[200 rows x 6 columns]"
            ],
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              "\n",
              "  <div id=\"df-c43a3c96-c85c-4e26-8f50-5c0727b1dc2c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>23</td>\n",
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              "      <td>25.355</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>18.043</td>\n",
              "      <td>drugY</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>11.567</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>12.006</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>M</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>9.894</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>M</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>14.020</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>11.349</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
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              "\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-c13735ae-6c1a-455d-9aa2-2b834e0b34d0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_86b2476b-82d8-4a23-9019-45f83fc81791\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        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",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_86b2476b-82d8-4a23-9019-45f83fc81791 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 15,\n        \"max\": 74,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          23,\n          49,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BP\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"HIGH\",\n          \"LOW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cholesterol\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NORMAL\",\n          \"HIGH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Na_to_K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.223955528459516,\n        \"min\": 6.269,\n        \"max\": 38.247,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          27.05,\n          9.281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Drug\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"drugC\",\n          \"drugB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:00.043872Z",
          "iopub.execute_input": "2024-09-20T04:55:00.04435Z",
          "iopub.status.idle": "2024-09-20T04:55:00.069758Z",
          "shell.execute_reply.started": "2024-09-20T04:55:00.044296Z",
          "shell.execute_reply": "2024-09-20T04:55:00.068325Z"
        },
        "trusted": true,
        "id": "zvfdyj867U5V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6234a2ea-7278-467a-bf8a-de811fc104e4"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Age          200 non-null    int64  \n",
            " 1   Sex          200 non-null    object \n",
            " 2   BP           200 non-null    object \n",
            " 3   Cholesterol  200 non-null    object \n",
            " 4   Na_to_K      200 non-null    float64\n",
            " 5   Drug         200 non-null    object \n",
            "dtypes: float64(1), int64(1), object(4)\n",
            "memory usage: 9.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:00.071825Z",
          "iopub.execute_input": "2024-09-20T04:55:00.072305Z",
          "iopub.status.idle": "2024-09-20T04:55:00.10214Z",
          "shell.execute_reply.started": "2024-09-20T04:55:00.072253Z",
          "shell.execute_reply": "2024-09-20T04:55:00.101012Z"
        },
        "trusted": true,
        "id": "B3t3aA3I7U5X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "56613b00-7d14-4fe4-9293-75c6120ea459"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age     Na_to_K\n",
              "count  200.000000  200.000000\n",
              "mean    44.315000   16.084485\n",
              "std     16.544315    7.223956\n",
              "min     15.000000    6.269000\n",
              "25%     31.000000   10.445500\n",
              "50%     45.000000   13.936500\n",
              "75%     58.000000   19.380000\n",
              "max     74.000000   38.247000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8564fa38-075e-4694-8759-520529953711\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Na_to_K</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>44.315000</td>\n",
              "      <td>16.084485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>16.544315</td>\n",
              "      <td>7.223956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>6.269000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>10.445500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>13.936500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>19.380000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>74.000000</td>\n",
              "      <td>38.247000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8564fa38-075e-4694-8759-520529953711')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    .colab-df-container {\n",
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              "\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",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "        document.querySelector('#df-8564fa38-075e-4694-8759-520529953711 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "          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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    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-c7975c1f-3921-4aa6-946e-7af891744f43 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\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.78957628560131,\n        \"min\": 15.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          44.315,\n          45.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Na_to_K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 65.85284973066084,\n        \"min\": 6.269,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          16.084485,\n          13.9365,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_data = data.select_dtypes(include=['number'])\n",
        "numeric_data.corr()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:00.103479Z",
          "iopub.execute_input": "2024-09-20T04:55:00.104104Z",
          "iopub.status.idle": "2024-09-20T04:55:00.12316Z",
          "shell.execute_reply.started": "2024-09-20T04:55:00.104064Z",
          "shell.execute_reply": "2024-09-20T04:55:00.121794Z"
        },
        "trusted": true,
        "id": "xGUVqAQ47U5Z",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "936bf990-917d-4b82-8270-cb481274f84e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age   Na_to_K\n",
              "Age      1.000000 -0.063119\n",
              "Na_to_K -0.063119  1.000000"
            ],
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              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3e840d09-d0cb-4ceb-80a2-60035bd60f0a');\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",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "    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-2319fb20-0a1d-46d8-9d9b-b22465919362 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\": \"numeric_data\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7517390057296423,\n        \"min\": -0.06311949726772587,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          -0.06311949726772587,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Na_to_K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7517390057296423,\n        \"min\": -0.06311949726772587,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          -0.06311949726772587\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax=plt.subplots(figsize=(5, 5))\n",
        "sns.heatmap(numeric_data.corr(), annot=True, linewidths=0.5, linecolor='black', fmt='.4f', ax=ax)\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:00.124744Z",
          "iopub.execute_input": "2024-09-20T04:55:00.125145Z",
          "iopub.status.idle": "2024-09-20T04:55:00.412959Z",
          "shell.execute_reply.started": "2024-09-20T04:55:00.125105Z",
          "shell.execute_reply": "2024-09-20T04:55:00.411855Z"
        },
        "trusted": true,
        "id": "Adl0afvC7U5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "outputId": "da071a37-9d92-4e4c-86dc-d2baeacea560"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "\n",
        "data.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "sns.pairplot(data, hue='Drug')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:00.41437Z",
          "iopub.execute_input": "2024-09-20T04:55:00.414796Z",
          "iopub.status.idle": "2024-09-20T04:55:02.630641Z",
          "shell.execute_reply.started": "2024-09-20T04:55:00.414734Z",
          "shell.execute_reply": "2024-09-20T04:55:02.629171Z"
        },
        "trusted": true,
        "id": "obAQyAiq7U5b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "7fca58ec-258f-41a4-8b6d-b647bc34d9e6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7ca84aaf7970>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 592x500 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:02.632345Z",
          "iopub.execute_input": "2024-09-20T04:55:02.632814Z",
          "iopub.status.idle": "2024-09-20T04:55:02.641748Z",
          "shell.execute_reply.started": "2024-09-20T04:55:02.632741Z",
          "shell.execute_reply": "2024-09-20T04:55:02.640089Z"
        },
        "trusted": true,
        "id": "rDIMIIgD7U5c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5a1a433-4851-46c2-d760-3c121bcb1d20"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K', 'Drug'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Age'].value_counts(dropna=False)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:02.645731Z",
          "iopub.execute_input": "2024-09-20T04:55:02.646224Z",
          "iopub.status.idle": "2024-09-20T04:55:02.662899Z",
          "shell.execute_reply.started": "2024-09-20T04:55:02.646159Z",
          "shell.execute_reply": "2024-09-20T04:55:02.661714Z"
        },
        "trusted": true,
        "id": "g7sSuK8Q7U5d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8fabf0ba-116f-4a8b-ee97-e415ffcc0dba"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age\n",
              "47    8\n",
              "23    7\n",
              "28    7\n",
              "49    7\n",
              "39    6\n",
              "32    6\n",
              "50    5\n",
              "37    5\n",
              "58    5\n",
              "60    5\n",
              "22    5\n",
              "34    4\n",
              "72    4\n",
              "51    4\n",
              "42    4\n",
              "26    4\n",
              "24    4\n",
              "74    4\n",
              "67    4\n",
              "68    4\n",
              "61    4\n",
              "56    4\n",
              "20    4\n",
              "36    4\n",
              "45    4\n",
              "41    4\n",
              "31    4\n",
              "43    4\n",
              "65    4\n",
              "57    4\n",
              "53    3\n",
              "40    3\n",
              "70    3\n",
              "59    3\n",
              "16    3\n",
              "38    3\n",
              "15    3\n",
              "69    3\n",
              "35    3\n",
              "18    3\n",
              "64    3\n",
              "52    2\n",
              "55    2\n",
              "62    2\n",
              "19    2\n",
              "29    2\n",
              "66    2\n",
              "73    2\n",
              "46    2\n",
              "48    2\n",
              "54    1\n",
              "17    1\n",
              "33    1\n",
              "63    1\n",
              "30    1\n",
              "21    1\n",
              "25    1\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
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      "source": [
        "data['Sex'].value_counts()"
      ],
      "metadata": {
        "execution": {
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      },
      "execution_count": 12,
      "outputs": [
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          "output_type": "execute_result",
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              "Sex\n",
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              "      <th>Sex</th>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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    {
      "cell_type": "code",
      "source": [
        "data['BP'].value_counts()"
      ],
      "metadata": {
        "execution": {
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          "height": 209
        },
        "outputId": "0766191a-7460-4c14-8b33-e6cb7e4d3d25"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BP\n",
              "HIGH      77\n",
              "LOW       64\n",
              "NORMAL    59\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "data['Cholesterol'].value_counts()"
      ],
      "metadata": {
        "execution": {
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          "iopub.execute_input": "2024-09-20T04:55:02.698011Z",
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        "id": "jKw7ycFb7U5g",
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          "height": 178
        },
        "outputId": "d168d615-6267-4e33-d287-24a4068155bd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "Cholesterol\n",
              "HIGH      103\n",
              "NORMAL     97\n",
              "Name: count, dtype: int64"
            ],
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              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cholesterol</th>\n",
              "      <th></th>\n",
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              "      <th>HIGH</th>\n",
              "      <td>103</td>\n",
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              "      <td>97</td>\n",
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              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 14
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    {
      "cell_type": "code",
      "source": [
        "data['Drug'].value_counts()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:02.711803Z",
          "iopub.execute_input": "2024-09-20T04:55:02.712189Z",
          "iopub.status.idle": "2024-09-20T04:55:02.728705Z",
          "shell.execute_reply.started": "2024-09-20T04:55:02.71215Z",
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        "trusted": true,
        "id": "P7ErXaON7U5g",
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          "height": 272
        },
        "outputId": "c2d8179d-c635-488c-ae3a-e900a94c8776"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Drug\n",
              "drugY    91\n",
              "drugX    54\n",
              "drugA    23\n",
              "drugC    16\n",
              "drugB    16\n",
              "Name: count, dtype: int64"
            ],
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              "      <td>23</td>\n",
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              "    <tr>\n",
              "      <th>drugC</th>\n",
              "      <td>16</td>\n",
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              "      <td>16</td>\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
          "execution_count": 15
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Visualization"
      ],
      "metadata": {
        "id": "pWvceHGJ7U5h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataAge=data['Age'].value_counts(dropna=False)\n",
        "npar_dataAge=np.array(dataAge)\n",
        "x=list(npar_dataAge)\n",
        "y=data.Age.value_counts().index\n",
        "\n",
        "DataAge={'Age':y, 'Number':x}\n",
        "DataAge=pd.DataFrame(DataAge)\n",
        "\n",
        "fig=px.bar(DataAge, x='Age', y='Number')\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:02.730233Z",
          "iopub.execute_input": "2024-09-20T04:55:02.730967Z",
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          "height": 542
        },
        "outputId": "04ed18aa-14f6-4e2b-e08f-adf88df6a0b8"
      },
      "execution_count": 16,
      "outputs": [
        {
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "colors=['gold', 'mediumturquoise']\n",
        "\n",
        "fig=go.Figure(data=[go.Pie(labels=['M', 'F'], values=[104, 96])])\n",
        "\n",
        "fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20, marker=dict(colors=colors, line=dict(color='#000000', width=2)))\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:04.909501Z",
          "iopub.execute_input": "2024-09-20T04:55:04.910003Z",
          "iopub.status.idle": "2024-09-20T04:55:04.97684Z",
          "shell.execute_reply.started": "2024-09-20T04:55:04.909965Z",
          "shell.execute_reply": "2024-09-20T04:55:04.97567Z"
        },
        "trusted": true,
        "id": "F70maYdN7U5i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "5597b17b-2d43-444c-9735-2581fe67e482"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "fig=px.bar(x=['HIGH', 'LOW', 'NORMAL'], y=[77, 64, 59])\n",
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          "iopub.status.busy": "2024-09-20T04:55:04.978197Z",
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    {
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      "source": [
        "fig=px.histogram(x=['HIGH', 'NORMAL'], y=[103, 97])\n",
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      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T04:55:05.042794Z",
          "iopub.execute_input": "2024-09-20T04:55:05.043272Z",
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          "shell.execute_reply.started": "2024-09-20T04:55:05.043219Z",
          "shell.execute_reply": "2024-09-20T04:55:05.144793Z"
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      "execution_count": 19,
      "outputs": [
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    {
      "cell_type": "code",
      "source": [
        "fig=go.Figure(data=[go.Pie(labels=['DrugY', 'DrugX', 'DrugA', 'DrugC', 'DrugB'], values=[91, 54, 23, 16, 16])])\n",
        "fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20, marker=dict(colors=px.colors.sequential.RdBu, line=dict(color='#000000', width=2)))\n",
        "fig.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:01:17.864779Z",
          "iopub.execute_input": "2024-09-20T05:01:17.865836Z",
          "iopub.status.idle": "2024-09-20T05:01:17.882081Z",
          "shell.execute_reply.started": "2024-09-20T05:01:17.865782Z",
          "shell.execute_reply": "2024-09-20T05:01:17.880831Z"
        },
        "trusted": true,
        "id": "NlNRO8kl7U5j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "17310c6c-c993-4b58-fab3-f14c7baf2c5f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "cell_type": "code",
      "source": [
        "fig=px.scatter(data, x='Na_to_K', y='Age', color='Drug', size='Age', hover_data=['Na_to_K'])\n",
        "fig.show()"
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      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:01:57.661677Z",
          "iopub.execute_input": "2024-09-20T05:01:57.662114Z",
          "iopub.status.idle": "2024-09-20T05:01:57.795235Z",
          "shell.execute_reply.started": "2024-09-20T05:01:57.662067Z",
          "shell.execute_reply": "2024-09-20T05:01:57.793755Z"
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        "trusted": true,
        "id": "R0XCSHKV7U5k",
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          "height": 542
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        "outputId": "953d3b00-d942-4e8a-859c-ffcdaeca294e"
      },
      "execution_count": 21,
      "outputs": [
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    {
      "cell_type": "markdown",
      "source": [
        "# Classifications Models"
      ],
      "metadata": {
        "id": "kEBSLbVw7U5l"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preparing"
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      "metadata": {
        "id": "3fxnzKfC7U5l"
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      "cell_type": "code",
      "source": [
        "dataclass=pd.read_csv('/kaggle/input/drug200/drug200.csv')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:04.657966Z",
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        "trusted": true,
        "id": "2az8zTAP7U5m"
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      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataclass"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:07.509233Z",
          "iopub.execute_input": "2024-09-20T05:14:07.509634Z",
          "iopub.status.idle": "2024-09-20T05:14:07.526464Z",
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          "shell.execute_reply": "2024-09-20T05:14:07.525259Z"
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        "trusted": true,
        "id": "K3iyIPSy7U5m",
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          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "ef52fb3a-979d-4442-950c-e8bbe19018eb"
      },
      "execution_count": 23,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age Sex      BP Cholesterol  Na_to_K   Drug\n",
              "0     23   F    HIGH        HIGH   25.355  drugY\n",
              "1     47   M     LOW        HIGH   13.093  drugC\n",
              "2     47   M     LOW        HIGH   10.114  drugC\n",
              "3     28   F  NORMAL        HIGH    7.798  drugX\n",
              "4     61   F     LOW        HIGH   18.043  drugY\n",
              "..   ...  ..     ...         ...      ...    ...\n",
              "195   56   F     LOW        HIGH   11.567  drugC\n",
              "196   16   M     LOW        HIGH   12.006  drugC\n",
              "197   52   M  NORMAL        HIGH    9.894  drugX\n",
              "198   23   M  NORMAL      NORMAL   14.020  drugX\n",
              "199   40   F     LOW      NORMAL   11.349  drugX\n",
              "\n",
              "[200 rows x 6 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>F</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>25.355</td>\n",
              "      <td>drugY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>18.043</td>\n",
              "      <td>drugY</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>11.567</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>12.006</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>M</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>9.894</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>M</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>14.020</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>11.349</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "\n",
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              "    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-4a4224ab-96ed-4dea-820e-47e2ab4a6b8f button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dataclass')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
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              "    <script>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dataclass');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataclass",
              "summary": "{\n  \"name\": \"dataclass\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 15,\n        \"max\": 74,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          23,\n          49,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BP\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"HIGH\",\n          \"LOW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cholesterol\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NORMAL\",\n          \"HIGH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Na_to_K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.223955528459516,\n        \"min\": 6.269,\n        \"max\": 38.247,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          27.05,\n          9.281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Drug\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"drugC\",\n          \"drugB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataclass.info()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:09.70006Z",
          "iopub.execute_input": "2024-09-20T05:14:09.700468Z",
          "iopub.status.idle": "2024-09-20T05:14:09.714161Z",
          "shell.execute_reply.started": "2024-09-20T05:14:09.70043Z",
          "shell.execute_reply": "2024-09-20T05:14:09.712887Z"
        },
        "trusted": true,
        "id": "_zSHdv9N7U5n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3dfd530e-7d09-4096-b2ed-fcdac88f5780"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Age          200 non-null    int64  \n",
            " 1   Sex          200 non-null    object \n",
            " 2   BP           200 non-null    object \n",
            " 3   Cholesterol  200 non-null    object \n",
            " 4   Na_to_K      200 non-null    float64\n",
            " 5   Drug         200 non-null    object \n",
            "dtypes: float64(1), int64(1), object(4)\n",
            "memory usage: 9.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataclass.Sex=[1 if i=='F' else 0 for i in dataclass.Sex]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:11.313079Z",
          "iopub.execute_input": "2024-09-20T05:14:11.313511Z",
          "iopub.status.idle": "2024-09-20T05:14:11.331307Z",
          "shell.execute_reply.started": "2024-09-20T05:14:11.313471Z",
          "shell.execute_reply": "2024-09-20T05:14:11.329473Z"
        },
        "trusted": true,
        "id": "1b9KgT4L7U5n"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataclass"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:14.011102Z",
          "iopub.execute_input": "2024-09-20T05:14:14.011541Z",
          "iopub.status.idle": "2024-09-20T05:14:14.028258Z",
          "shell.execute_reply.started": "2024-09-20T05:14:14.011499Z",
          "shell.execute_reply": "2024-09-20T05:14:14.02686Z"
        },
        "trusted": true,
        "id": "YLJlON_67U5o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "27ed4dc1-ac18-47c2-8d2c-6dfa35b6670d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Sex      BP Cholesterol  Na_to_K   Drug\n",
              "0     23    1    HIGH        HIGH   25.355  drugY\n",
              "1     47    0     LOW        HIGH   13.093  drugC\n",
              "2     47    0     LOW        HIGH   10.114  drugC\n",
              "3     28    1  NORMAL        HIGH    7.798  drugX\n",
              "4     61    1     LOW        HIGH   18.043  drugY\n",
              "..   ...  ...     ...         ...      ...    ...\n",
              "195   56    1     LOW        HIGH   11.567  drugC\n",
              "196   16    0     LOW        HIGH   12.006  drugC\n",
              "197   52    0  NORMAL        HIGH    9.894  drugX\n",
              "198   23    0  NORMAL      NORMAL   14.020  drugX\n",
              "199   40    1     LOW      NORMAL   11.349  drugX\n",
              "\n",
              "[200 rows x 6 columns]"
            ],
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              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>25.355</td>\n",
              "      <td>drugY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>18.043</td>\n",
              "      <td>drugY</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>11.567</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>12.006</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>9.894</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>14.020</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>LOW</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>11.349</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
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              "\n",
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              "        document.querySelector('#df-373d9c27-e8e2-4d31-beaf-611cd0ae8c42 button.colab-df-convert');\n",
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              "        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",
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              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "      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-07df72a9-5dd2-46fb-a02d-c209d656ee99 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f27edec6-ba6c-4dd4-a53c-162af72b160f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border-radius: 50%;\n",
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              "        display: none;\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        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",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dataclass')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f27edec6-ba6c-4dd4-a53c-162af72b160f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dataclass');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataclass",
              "summary": "{\n  \"name\": \"dataclass\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 15,\n        \"max\": 74,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          23,\n          49,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BP\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"HIGH\",\n          \"LOW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cholesterol\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NORMAL\",\n          \"HIGH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Na_to_K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.223955528459516,\n        \"min\": 6.269,\n        \"max\": 38.247,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          27.05,\n          9.281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Drug\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"drugC\",\n          \"drugB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "for i in range(0, len(dataclass.BP)):\n",
        "    if dataclass.BP[i]=='LOW':\n",
        "        dataclass.BP[i]=2\n",
        "\n",
        "    elif dataclass.BP[i]=='NORMAL':\n",
        "        dataclass.BP[i]=1\n",
        "    else:\n",
        "        dataclass.BP[i]=0"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:16.252653Z",
          "iopub.execute_input": "2024-09-20T05:14:16.253696Z",
          "iopub.status.idle": "2024-09-20T05:14:16.354823Z",
          "shell.execute_reply.started": "2024-09-20T05:14:16.253557Z",
          "shell.execute_reply": "2024-09-20T05:14:16.353608Z"
        },
        "trusted": true,
        "id": "iiCWNdVA7U5p"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataclass"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:18.512518Z",
          "iopub.execute_input": "2024-09-20T05:14:18.512981Z",
          "iopub.status.idle": "2024-09-20T05:14:18.528062Z",
          "shell.execute_reply.started": "2024-09-20T05:14:18.512937Z",
          "shell.execute_reply": "2024-09-20T05:14:18.526951Z"
        },
        "trusted": true,
        "id": "FCfaYBnn7U5q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "cd4923f3-bc0e-42cc-ba2f-5527873f3693"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Sex BP Cholesterol  Na_to_K   Drug\n",
              "0     23    1  0        HIGH   25.355  drugY\n",
              "1     47    0  2        HIGH   13.093  drugC\n",
              "2     47    0  2        HIGH   10.114  drugC\n",
              "3     28    1  1        HIGH    7.798  drugX\n",
              "4     61    1  2        HIGH   18.043  drugY\n",
              "..   ...  ... ..         ...      ...    ...\n",
              "195   56    1  2        HIGH   11.567  drugC\n",
              "196   16    0  2        HIGH   12.006  drugC\n",
              "197   52    0  1        HIGH    9.894  drugX\n",
              "198   23    0  1      NORMAL   14.020  drugX\n",
              "199   40    1  2      NORMAL   11.349  drugX\n",
              "\n",
              "[200 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d50e41ce-69df-4320-834e-a8b0fcfb5be5\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>25.355</td>\n",
              "      <td>drugY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>18.043</td>\n",
              "      <td>drugY</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>11.567</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>12.006</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>9.894</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>14.020</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>11.349</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      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-3357b87a-10cf-45d2-b619-e986c570042e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_da488bc3-f349-4021-9cfb-a98d0495b9e5\">\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate: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",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dataclass')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_da488bc3-f349-4021-9cfb-a98d0495b9e5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dataclass');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataclass",
              "summary": "{\n  \"name\": \"dataclass\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 15,\n        \"max\": 74,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          23,\n          49,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BP\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cholesterol\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"NORMAL\",\n          \"HIGH\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Na_to_K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.223955528459516,\n        \"min\": 6.269,\n        \"max\": 38.247,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          27.05,\n          9.281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Drug\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"drugC\",\n          \"drugB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataclass.Cholesterol=[1 if i=='HIGH' else 0 for i in dataclass.Cholesterol]"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:21.186904Z",
          "iopub.execute_input": "2024-09-20T05:14:21.187318Z",
          "iopub.status.idle": "2024-09-20T05:14:21.193615Z",
          "shell.execute_reply.started": "2024-09-20T05:14:21.187279Z",
          "shell.execute_reply": "2024-09-20T05:14:21.192159Z"
        },
        "trusted": true,
        "id": "YMH2sNor7U5q"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataclass"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:23.390575Z",
          "iopub.execute_input": "2024-09-20T05:14:23.391812Z",
          "iopub.status.idle": "2024-09-20T05:14:23.4073Z",
          "shell.execute_reply.started": "2024-09-20T05:14:23.391728Z",
          "shell.execute_reply": "2024-09-20T05:14:23.406089Z"
        },
        "trusted": true,
        "id": "rWyNpT6Y7U5u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "9191c157-b310-4e9a-c98e-02625d9a7943"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Sex BP  Cholesterol  Na_to_K   Drug\n",
              "0     23    1  0            1   25.355  drugY\n",
              "1     47    0  2            1   13.093  drugC\n",
              "2     47    0  2            1   10.114  drugC\n",
              "3     28    1  1            1    7.798  drugX\n",
              "4     61    1  2            1   18.043  drugY\n",
              "..   ...  ... ..          ...      ...    ...\n",
              "195   56    1  2            1   11.567  drugC\n",
              "196   16    0  2            1   12.006  drugC\n",
              "197   52    0  1            1    9.894  drugX\n",
              "198   23    0  1            0   14.020  drugX\n",
              "199   40    1  2            0   11.349  drugX\n",
              "\n",
              "[200 rows x 6 columns]"
            ],
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              "    <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
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              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>18.043</td>\n",
              "      <td>drugY</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>11.567</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>12.006</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9.894</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>14.020</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>11.349</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\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",
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              "  @keyframes spin {\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\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",
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              "    (() => {\n",
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              "        document.querySelector('#df-264646c8-03a3-4f0b-b076-e8cc2f210850 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_05a23de6-f890-4643-bcf4-05344154d684\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
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              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dataclass')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_05a23de6-f890-4643-bcf4-05344154d684 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dataclass');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataclass",
              "summary": "{\n  \"name\": \"dataclass\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 15,\n        \"max\": 74,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          23,\n          49,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BP\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cholesterol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Na_to_K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.223955528459516,\n        \"min\": 6.269,\n        \"max\": 38.247,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          27.05,\n          9.281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Drug\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"drugC\",\n          \"drugB\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "for i in range(0, len(dataclass)):\n",
        "    if dataclass.Drug[i]=='drugY':\n",
        "        dataclass.Drug[i]=4\n",
        "    elif dataclass.Drug[i]=='drugX':\n",
        "        dataclass.Drug[i]=3\n",
        "    elif dataclass.Drug[i]=='drugA':\n",
        "        dataclass.Drug[i]=2\n",
        "    elif dataclass.Drug[i]=='drugC':\n",
        "        dataclass.Drug[i]=1\n",
        "    else:\n",
        "        dataclass.Drug[i]=0"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:25.151503Z",
          "iopub.execute_input": "2024-09-20T05:14:25.151963Z",
          "iopub.status.idle": "2024-09-20T05:14:25.234398Z",
          "shell.execute_reply.started": "2024-09-20T05:14:25.151919Z",
          "shell.execute_reply": "2024-09-20T05:14:25.233131Z"
        },
        "trusted": true,
        "id": "kZlNqb5O7U5u"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataclass"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:27.51392Z",
          "iopub.execute_input": "2024-09-20T05:14:27.514323Z",
          "iopub.status.idle": "2024-09-20T05:14:27.531033Z",
          "shell.execute_reply.started": "2024-09-20T05:14:27.514286Z",
          "shell.execute_reply": "2024-09-20T05:14:27.529806Z"
        },
        "trusted": true,
        "id": "AUgIIQlf7U5v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "8b97dfcc-db9c-4f43-afd6-2935c8591654"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Sex BP  Cholesterol  Na_to_K Drug\n",
              "0     23    1  0            1   25.355    4\n",
              "1     47    0  2            1   13.093    1\n",
              "2     47    0  2            1   10.114    1\n",
              "3     28    1  1            1    7.798    3\n",
              "4     61    1  2            1   18.043    4\n",
              "..   ...  ... ..          ...      ...  ...\n",
              "195   56    1  2            1   11.567    1\n",
              "196   16    0  2            1   12.006    1\n",
              "197   52    0  1            1    9.894    3\n",
              "198   23    0  1            0   14.020    3\n",
              "199   40    1  2            0   11.349    3\n",
              "\n",
              "[200 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
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              "      <th>0</th>\n",
              "      <td>23</td>\n",
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              "      <td>25.355</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>13.093</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>10.114</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7.798</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>18.043</td>\n",
              "      <td>4</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>11.567</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>12.006</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9.894</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>14.020</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>11.349</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\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",
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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",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-9cf9dd45-7368-42f4-a1b4-0c75669d92cd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
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              "        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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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </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",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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",
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              "    20% {\n",
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              "      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-f82452fc-7cde-43b9-81bf-edd012f1aa0e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_a84e06b7-4ff7-4154-b65f-c94c722632d2\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
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              "        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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      }\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dataclass')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_a84e06b7-4ff7-4154-b65f-c94c722632d2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dataclass');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataclass",
              "summary": "{\n  \"name\": \"dataclass\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 15,\n        \"max\": 74,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          23,\n          49,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BP\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cholesterol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Na_to_K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.223955528459516,\n        \"min\": 6.269,\n        \"max\": 38.247,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          27.05,\n          9.281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Drug\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataclass.info()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:30.000649Z",
          "iopub.execute_input": "2024-09-20T05:14:30.001155Z",
          "iopub.status.idle": "2024-09-20T05:14:30.013196Z",
          "shell.execute_reply.started": "2024-09-20T05:14:30.001109Z",
          "shell.execute_reply": "2024-09-20T05:14:30.011961Z"
        },
        "trusted": true,
        "id": "K5h7tpOS7U5v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebd34e01-8223-4e31-f525-5a0ba475714f"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Age          200 non-null    int64  \n",
            " 1   Sex          200 non-null    int64  \n",
            " 2   BP           200 non-null    object \n",
            " 3   Cholesterol  200 non-null    int64  \n",
            " 4   Na_to_K      200 non-null    float64\n",
            " 5   Drug         200 non-null    object \n",
            "dtypes: float64(1), int64(3), object(2)\n",
            "memory usage: 9.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_types_dict={'BP':int, 'Drug':int}\n",
        "dataclass=dataclass.astype(data_types_dict)\n",
        "dataclass.info()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:31.755337Z",
          "iopub.execute_input": "2024-09-20T05:14:31.755749Z",
          "iopub.status.idle": "2024-09-20T05:14:31.770895Z",
          "shell.execute_reply.started": "2024-09-20T05:14:31.755706Z",
          "shell.execute_reply": "2024-09-20T05:14:31.769593Z"
        },
        "trusted": true,
        "id": "9kAeOVBq7U5w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "903eb20c-b709-4e92-bad3-053016e0ee80"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   Age          200 non-null    int64  \n",
            " 1   Sex          200 non-null    int64  \n",
            " 2   BP           200 non-null    int64  \n",
            " 3   Cholesterol  200 non-null    int64  \n",
            " 4   Na_to_K      200 non-null    float64\n",
            " 5   Drug         200 non-null    int64  \n",
            "dtypes: float64(1), int64(5)\n",
            "memory usage: 9.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_data=dataclass.drop(['Drug'], axis=1)\n",
        "y_data=dataclass.Drug.values"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:33.396694Z",
          "iopub.execute_input": "2024-09-20T05:14:33.39719Z",
          "iopub.status.idle": "2024-09-20T05:14:33.403998Z",
          "shell.execute_reply.started": "2024-09-20T05:14:33.397139Z",
          "shell.execute_reply": "2024-09-20T05:14:33.402819Z"
        },
        "trusted": true,
        "id": "SxNVBQeZ7U5x"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_data"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:35.356694Z",
          "iopub.execute_input": "2024-09-20T05:14:35.357228Z",
          "iopub.status.idle": "2024-09-20T05:14:35.372295Z",
          "shell.execute_reply.started": "2024-09-20T05:14:35.357179Z",
          "shell.execute_reply": "2024-09-20T05:14:35.371026Z"
        },
        "trusted": true,
        "id": "X96pCPWa7U58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "ee389952-387f-4ac3-b647-ad3766261b53"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Age  Sex  BP  Cholesterol  Na_to_K\n",
              "0     23    1   0            1   25.355\n",
              "1     47    0   2            1   13.093\n",
              "2     47    0   2            1   10.114\n",
              "3     28    1   1            1    7.798\n",
              "4     61    1   2            1   18.043\n",
              "..   ...  ...  ..          ...      ...\n",
              "195   56    1   2            1   11.567\n",
              "196   16    0   2            1   12.006\n",
              "197   52    0   1            1    9.894\n",
              "198   23    0   1            0   14.020\n",
              "199   40    1   2            0   11.349\n",
              "\n",
              "[200 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d61f1d2-1630-4f06-bbda-e3d3f5d323c7\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>25.355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>13.093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>10.114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7.798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>18.043</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>11.567</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>12.006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>52</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9.894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>23</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>14.020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>40</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>11.349</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4d61f1d2-1630-4f06-bbda-e3d3f5d323c7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "    animation:\n",
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              "      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-805f3b1a-f9ba-4678-a718-9c22ba6bf4d8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
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              "\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('x_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d412ecfb-ce85-41f3-aeaf-a6762b1d96f7 button.colab-df-generate');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('x_data');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_data",
              "summary": "{\n  \"name\": \"x_data\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16,\n        \"min\": 15,\n        \"max\": 74,\n        \"num_unique_values\": 57,\n        \"samples\": [\n          23,\n          49,\n          37\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cholesterol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Na_to_K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.223955528459516,\n        \"min\": 6.269,\n        \"max\": 38.247,\n        \"num_unique_values\": 198,\n        \"samples\": [\n          27.05,\n          9.281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_data"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:37.195631Z",
          "iopub.execute_input": "2024-09-20T05:14:37.196199Z",
          "iopub.status.idle": "2024-09-20T05:14:37.205135Z",
          "shell.execute_reply.started": "2024-09-20T05:14:37.196147Z",
          "shell.execute_reply": "2024-09-20T05:14:37.203621Z"
        },
        "trusted": true,
        "id": "x1AzdwaV7U59",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "959f5d9b-af53-423e-f90d-616c4e81a2b1"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 1, 1, 3, 4, 3, 4, 1, 4, 4, 1, 4, 4, 4, 3, 4, 3, 2, 1, 4, 4, 4,\n",
              "       4, 4, 4, 4, 4, 3, 4, 4, 3, 0, 3, 4, 3, 3, 2, 3, 3, 3, 4, 0, 4, 3,\n",
              "       3, 3, 2, 1, 4, 4, 4, 3, 4, 4, 0, 1, 0, 4, 3, 4, 4, 2, 4, 3, 0, 4,\n",
              "       2, 3, 4, 4, 0, 4, 3, 4, 4, 4, 2, 4, 2, 3, 0, 3, 1, 2, 1, 0, 3, 4,\n",
              "       4, 4, 4, 4, 4, 4, 4, 3, 4, 4, 4, 4, 2, 2, 1, 3, 4, 3, 3, 4, 0, 4,\n",
              "       2, 3, 3, 3, 3, 4, 3, 3, 2, 4, 4, 4, 4, 4, 0, 4, 4, 3, 4, 3, 4, 4,\n",
              "       3, 4, 4, 3, 0, 2, 0, 3, 2, 4, 0, 4, 2, 3, 3, 2, 3, 1, 2, 0, 3, 3,\n",
              "       4, 1, 2, 4, 1, 3, 3, 0, 3, 4, 4, 4, 4, 3, 4, 2, 3, 3, 4, 4, 2, 4,\n",
              "       2, 4, 4, 4, 4, 3, 3, 4, 4, 4, 0, 2, 4, 4, 4, 2, 4, 1, 4, 1, 1, 3,\n",
              "       3, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "x_train, x_test, y_train, y_test=train_test_split(x_data, y_data, test_size=0.3, random_state=1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:39.134997Z",
          "iopub.execute_input": "2024-09-20T05:14:39.13587Z",
          "iopub.status.idle": "2024-09-20T05:14:39.14632Z",
          "shell.execute_reply.started": "2024-09-20T05:14:39.135816Z",
          "shell.execute_reply": "2024-09-20T05:14:39.144876Z"
        },
        "trusted": true,
        "id": "0XmqzVHa7U59"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decision Tree Classifier\n",
        "Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome."
      ],
      "metadata": {
        "id": "sDvJaxve7U5-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn import metrics\n",
        "\n",
        "dtc=DecisionTreeClassifier()\n",
        "\n",
        "dtc.fit(x_train, y_train)\n",
        "predict=dtc.predict(x_test)\n",
        "print('The accuracy of the Decision Tree is', metrics.accuracy_score(predict, y_test))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:14:45.928102Z",
          "iopub.execute_input": "2024-09-20T05:14:45.928484Z",
          "iopub.status.idle": "2024-09-20T05:14:45.94225Z",
          "shell.execute_reply.started": "2024-09-20T05:14:45.928446Z",
          "shell.execute_reply": "2024-09-20T05:14:45.941085Z"
        },
        "trusted": true,
        "id": "Ll82HF5b7U5_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c81f2ed-8960-4026-d0c9-9ca9bba505b0"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the Decision Tree is 0.9666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DecisionTreeClassifier with 'gini'"
      ],
      "metadata": {
        "id": "AgH_rokg7U5_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DTC_gini=DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=0)\n",
        "DTC_gini.fit(x_train, y_train)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:15:56.097223Z",
          "iopub.execute_input": "2024-09-20T05:15:56.097646Z",
          "iopub.status.idle": "2024-09-20T05:15:56.11329Z",
          "shell.execute_reply.started": "2024-09-20T05:15:56.097604Z",
          "shell.execute_reply": "2024-09-20T05:15:56.111885Z"
        },
        "trusted": true,
        "id": "J3_rb1u77U6A",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "799b3d94-ea05-4e84-e4d7-fe0a8427d113"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=3, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=3, random_state=0)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=3, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_gini=DTC_gini.predict(x_test)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:16:13.448804Z",
          "iopub.execute_input": "2024-09-20T05:16:13.449266Z",
          "iopub.status.idle": "2024-09-20T05:16:13.456963Z",
          "shell.execute_reply.started": "2024-09-20T05:16:13.449223Z",
          "shell.execute_reply": "2024-09-20T05:16:13.455743Z"
        },
        "trusted": true,
        "id": "a1Khxv5K7U6B"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "print('Model accuracy score with criterion gini index : {0:0.4f}'.format(accuracy_score(y_test, y_pred_gini)))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:17:01.772122Z",
          "iopub.execute_input": "2024-09-20T05:17:01.773077Z",
          "iopub.status.idle": "2024-09-20T05:17:01.780616Z",
          "shell.execute_reply.started": "2024-09-20T05:17:01.773019Z",
          "shell.execute_reply": "2024-09-20T05:17:01.779321Z"
        },
        "trusted": true,
        "id": "Ug8eoRU47U6C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1c9ff00-e66c-4701-f421-7f24fc071877"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score with criterion gini index : 0.9000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train_gini=DTC_gini.predict(x_train)\n",
        "y_pred_train_gini"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:17:19.792579Z",
          "iopub.execute_input": "2024-09-20T05:17:19.793024Z",
          "iopub.status.idle": "2024-09-20T05:17:19.803934Z",
          "shell.execute_reply.started": "2024-09-20T05:17:19.792983Z",
          "shell.execute_reply": "2024-09-20T05:17:19.802719Z"
        },
        "trusted": true,
        "id": "BMX6C6IL7U6C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c3ad063-adce-4ef3-f129-b9e3b15cebe6"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 3, 2, 4, 2, 3, 3, 3, 4, 2, 0, 3, 4, 3, 4, 3, 4, 3, 4, 4, 4, 4,\n",
              "       3, 4, 2, 4, 3, 3, 2, 3, 4, 2, 0, 0, 3, 3, 3, 3, 3, 4, 4, 4, 4, 3,\n",
              "       3, 0, 0, 2, 4, 3, 4, 3, 4, 4, 3, 3, 4, 4, 2, 4, 4, 4, 2, 3, 4, 4,\n",
              "       4, 3, 4, 3, 3, 2, 3, 2, 4, 4, 4, 4, 3, 4, 0, 3, 3, 4, 0, 4, 0, 4,\n",
              "       4, 0, 4, 4, 2, 3, 4, 3, 2, 4, 3, 4, 4, 3, 4, 4, 4, 4, 4, 4, 3, 4,\n",
              "       4, 2, 3, 3, 3, 4, 3, 4, 4, 4, 0, 4, 2, 3, 3, 2, 4, 4, 4, 4, 4, 3,\n",
              "       2, 4, 3, 4, 2, 3, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training-set accuracy score : {0:0.4f}'.format(accuracy_score(y_train, y_pred_train_gini)))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:17:45.205729Z",
          "iopub.execute_input": "2024-09-20T05:17:45.206198Z",
          "iopub.status.idle": "2024-09-20T05:17:45.214143Z",
          "shell.execute_reply.started": "2024-09-20T05:17:45.206155Z",
          "shell.execute_reply": "2024-09-20T05:17:45.212655Z"
        },
        "trusted": true,
        "id": "oo5GRYAt7U6D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7857bae0-9d33-4715-d9db-59bf6a8dadac"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training-set accuracy score : 0.9143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training set score : {:.4f}'.format(DTC_gini.score(x_train, y_train)))\n",
        "print('Test set score : {:.4f}'.format(DTC_gini.score(x_test, y_test)))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:18:32.178839Z",
          "iopub.execute_input": "2024-09-20T05:18:32.179316Z",
          "iopub.status.idle": "2024-09-20T05:18:32.193743Z",
          "shell.execute_reply.started": "2024-09-20T05:18:32.179273Z",
          "shell.execute_reply": "2024-09-20T05:18:32.192451Z"
        },
        "trusted": true,
        "id": "7_Q6VEBM7U6D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a58b39a4-a06a-4cfe-f780-263a70e06e1c"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set score : 0.9143\n",
            "Test set score : 0.9000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "DecisionTreeClassifier with 'entropy'"
      ],
      "metadata": {
        "id": "iIB9CuvQ7U6D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DTC_en=DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)\n",
        "DTC_en.fit(x_train, y_train)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:19:17.168624Z",
          "iopub.execute_input": "2024-09-20T05:19:17.169215Z",
          "iopub.status.idle": "2024-09-20T05:19:17.18519Z",
          "shell.execute_reply.started": "2024-09-20T05:19:17.169157Z",
          "shell.execute_reply": "2024-09-20T05:19:17.183839Z"
        },
        "trusted": true,
        "id": "kUK-nDKs7U6E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "766fede4-5df7-462e-9808-e839f39c7643"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(criterion='entropy', max_depth=3, random_state=0)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=0)</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=3, random_state=0)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_en=DTC_en.predict(x_test)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:19:36.040149Z",
          "iopub.execute_input": "2024-09-20T05:19:36.040556Z",
          "iopub.status.idle": "2024-09-20T05:19:36.048203Z",
          "shell.execute_reply.started": "2024-09-20T05:19:36.040517Z",
          "shell.execute_reply": "2024-09-20T05:19:36.046837Z"
        },
        "trusted": true,
        "id": "C3j-8APo7U6E"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Model accuracy score with criterion entropy : {0:0.4f}'.format(accuracy_score(y_test, y_pred_en)))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:20:04.772229Z",
          "iopub.execute_input": "2024-09-20T05:20:04.772682Z",
          "iopub.status.idle": "2024-09-20T05:20:04.780254Z",
          "shell.execute_reply.started": "2024-09-20T05:20:04.772638Z",
          "shell.execute_reply": "2024-09-20T05:20:04.778882Z"
        },
        "trusted": true,
        "id": "lLp9Uqxm7U6E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a5e36cb-636a-4f1d-c4b8-47d89d253cd2"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score with criterion entropy : 0.9000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train_en=DTC_en.predict(x_train)\n",
        "y_pred_train_en"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:20:37.284638Z",
          "iopub.execute_input": "2024-09-20T05:20:37.285091Z",
          "iopub.status.idle": "2024-09-20T05:20:37.295191Z",
          "shell.execute_reply.started": "2024-09-20T05:20:37.285049Z",
          "shell.execute_reply": "2024-09-20T05:20:37.294005Z"
        },
        "trusted": true,
        "id": "Re8mFjX27U6E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd85d5ce-ccd4-408b-9334-6bdeee1dc7c1"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 3, 2, 4, 2, 3, 3, 3, 4, 2, 0, 3, 4, 3, 4, 3, 4, 3, 4, 4, 4, 4,\n",
              "       3, 4, 2, 4, 3, 3, 2, 3, 4, 2, 0, 0, 3, 3, 3, 3, 3, 4, 4, 4, 4, 3,\n",
              "       3, 0, 0, 2, 4, 3, 4, 3, 4, 4, 3, 3, 4, 4, 2, 4, 4, 4, 2, 3, 4, 4,\n",
              "       4, 3, 4, 3, 3, 2, 3, 2, 4, 4, 4, 4, 3, 4, 0, 3, 3, 4, 0, 4, 0, 4,\n",
              "       4, 0, 4, 4, 2, 3, 4, 3, 2, 4, 3, 4, 4, 3, 4, 4, 4, 4, 4, 4, 3, 4,\n",
              "       4, 2, 3, 3, 3, 4, 3, 4, 4, 4, 0, 4, 2, 3, 3, 2, 4, 4, 4, 4, 4, 3,\n",
              "       2, 4, 3, 4, 2, 3, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training-set accuracy score : {0:0.4f}'.format(accuracy_score(y_train, y_pred_train_en)))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:21:06.59643Z",
          "iopub.execute_input": "2024-09-20T05:21:06.596852Z",
          "iopub.status.idle": "2024-09-20T05:21:06.604837Z",
          "shell.execute_reply.started": "2024-09-20T05:21:06.596811Z",
          "shell.execute_reply": "2024-09-20T05:21:06.602678Z"
        },
        "trusted": true,
        "id": "aAnmlwV77U6E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55b0ec60-32e6-4d25-91aa-90bb0f4ee5f5"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training-set accuracy score : 0.9143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training set score : {:.4f}'.format(DTC_en.score(x_train, y_train)))\n",
        "print('Test set score : {:.4f}'.format(DTC_en.score(x_test, y_test)))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:21:51.539549Z",
          "iopub.execute_input": "2024-09-20T05:21:51.540061Z",
          "iopub.status.idle": "2024-09-20T05:21:51.553446Z",
          "shell.execute_reply.started": "2024-09-20T05:21:51.540012Z",
          "shell.execute_reply": "2024-09-20T05:21:51.552014Z"
        },
        "trusted": true,
        "id": "TWkcqWqy7U6F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1560ad72-415a-41a2-f398-c664e1be37d1"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set score : 0.9143\n",
            "Test set score : 0.9000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## RandomForest Classifier\n",
        "Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It can be used for both Classification and Regression problems in ML. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model."
      ],
      "metadata": {
        "id": "B_ehb6M97U6F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rfc=RandomForestClassifier(random_state=0)\n",
        "\n",
        "rfc.fit(x_train, y_train)\n",
        "\n",
        "predict=rfc.predict(x_test)\n",
        "\n",
        "print('The accuracy of the Random Forest is', metrics.accuracy_score(predict, y_test))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:23:36.539148Z",
          "iopub.execute_input": "2024-09-20T05:23:36.539575Z",
          "iopub.status.idle": "2024-09-20T05:23:36.729914Z",
          "shell.execute_reply.started": "2024-09-20T05:23:36.539522Z",
          "shell.execute_reply": "2024-09-20T05:23:36.728829Z"
        },
        "trusted": true,
        "id": "Gt29ihx77U6F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8516e466-ad8c-4683-ff58-523943c76f99"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the Random Forest is 0.95\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RandomForest Classifier with 'n_estimators=100'"
      ],
      "metadata": {
        "id": "phwqu-RT7U6G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rfc_100=RandomForestClassifier(n_estimators=100, random_state=0)\n",
        "\n",
        "rfc_100.fit(x_train, y_train)\n",
        "predict=rfc_100.predict(x_test)\n",
        "print('The accuracy of the Random Forest is', metrics.accuracy_score(predict, y_test))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:25:40.312783Z",
          "iopub.execute_input": "2024-09-20T05:25:40.313758Z",
          "iopub.status.idle": "2024-09-20T05:25:40.498304Z",
          "shell.execute_reply.started": "2024-09-20T05:25:40.313712Z",
          "shell.execute_reply": "2024-09-20T05:25:40.497057Z"
        },
        "trusted": true,
        "id": "9y8YCQiL7U6G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55370932-d2b4-4352-e180-91f29461c0cd"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the Random Forest is 0.95\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_score_list=[]\n",
        "train_score_list=[]\n",
        "\n",
        "for i in range(0, 10):\n",
        "    rfc2=RandomForestClassifier(random_state=i)\n",
        "    rfc2.fit(x_train, y_train)\n",
        "    test_score_list.append(rfc2.score(x_test, y_test))\n",
        "    train_score_list.append(rfc2.score(x_train, y_train))\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "p=sns.lineplot(x=range(0, 10), y=train_score_list, marker='*', label='Train Score')\n",
        "p=sns.lineplot(x=range(0, 10), y=test_score_list, marker='o', label='Test Score')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:28:15.729437Z",
          "iopub.execute_input": "2024-09-20T05:28:15.729899Z",
          "iopub.status.idle": "2024-09-20T05:28:17.993337Z",
          "shell.execute_reply.started": "2024-09-20T05:28:15.729855Z",
          "shell.execute_reply": "2024-09-20T05:28:17.991961Z"
        },
        "trusted": true,
        "id": "q3mcR7iH7U6G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "eb24e96a-4e74-4bf7-8a89-d5b1ac5c042e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_score_list=[]\n",
        "train_score_list=[]\n",
        "\n",
        "list_n_estimators=[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
        "\n",
        "for i in range(0, len(list_n_estimators)):\n",
        "    rfc3=RandomForestClassifier(n_estimators=list_n_estimators[i], random_state=5)\n",
        "    rfc3.fit(x_train, y_train)\n",
        "    test_score_list.append(rfc3.score(x_test, y_test))\n",
        "    train_score_list.append(rfc3.score(x_train, y_train))\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "p=sns.lineplot(x=range(0, len(list_n_estimators)), y=train_score_list, marker='*', label='Train Score')\n",
        "p=sns.lineplot(x=range(0, len(list_n_estimators)), y=test_score_list, marker='o', label='Test Score')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-09-20T05:31:19.011636Z",
          "iopub.execute_input": "2024-09-20T05:31:19.012167Z",
          "iopub.status.idle": "2024-09-20T05:31:20.431474Z",
          "shell.execute_reply.started": "2024-09-20T05:31:19.012115Z",
          "shell.execute_reply": "2024-09-20T05:31:20.430212Z"
        },
        "trusted": true,
        "id": "oD_rNbG97U6G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "cdf8bcef-ba7b-46ec-860b-c21e0433221e"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RandomForest Classifier with the best parameters"
      ],
      "metadata": {
        "id": "NhuU7xn17U6G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "last_rfc=RandomForestClassifier(n_estimators=100, random_state=5)\n",
        "last_rfc.fit(x_train, y_train)\n",
        "predict=last_rfc.predict(x_test)\n",
        "print('The accuracy of the Random Forest is', metrics.accuracy_score(predict, y_test))"
      ],
      "metadata": {
        "id": "lgKoIPec7U6H",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afce976c-a5b4-4e22-dea6-43e96e4d6ee7"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the Random Forest is 0.9666666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_en=last_rfc.predict(x_test)\n",
        "print('Model accuracy score with best parameters : {0:0.4f}'.format(accuracy_score(y_test, y_pred_en)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpPObbwlNKhF",
        "outputId": "a047629f-aba2-4821-8a53-b5ab11755096"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model accuracy score with best parameters : 0.9667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train_en=last_rfc.predict(x_train)\n",
        "y_pred_train_en"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0zSv5dICNVpw",
        "outputId": "170c4b3b-f045-478a-e5f7-e42c8fe55ec5"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 3, 2, 4, 2, 3, 3, 3, 4, 2, 0, 1, 4, 1, 4, 3, 4, 3, 4, 4, 4, 4,\n",
              "       3, 4, 2, 4, 1, 3, 2, 3, 4, 2, 0, 0, 3, 3, 1, 3, 3, 4, 4, 4, 4, 3,\n",
              "       1, 0, 0, 2, 4, 3, 4, 1, 4, 4, 1, 3, 4, 4, 2, 4, 4, 4, 2, 3, 4, 4,\n",
              "       4, 3, 4, 1, 3, 2, 3, 2, 4, 4, 4, 4, 3, 4, 0, 3, 3, 4, 0, 4, 0, 4,\n",
              "       4, 0, 4, 4, 2, 3, 4, 3, 2, 4, 1, 4, 4, 3, 4, 4, 4, 4, 4, 4, 1, 4,\n",
              "       4, 2, 3, 1, 1, 4, 3, 4, 4, 4, 0, 4, 2, 3, 3, 2, 4, 4, 4, 4, 4, 3,\n",
              "       2, 4, 3, 4, 2, 3, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training-set accuracy score : {0:0.4f}'.format(accuracy_score(y_train, y_pred_train_en)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLiT4DCFNaE1",
        "outputId": "b43648e9-a3b5-4f2f-a301-be3a16189e8c"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training-set accuracy score : 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Training set score : {:.4f}'.format(last_rfc.score(x_train, y_train)))\n",
        "print('Test set score : {:.4f}'.format(last_rfc.score(x_test, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RcBtlpUPNgql",
        "outputId": "4b276718-9d16-416a-af1e-94f1d1e027f0"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set score : 1.0000\n",
            "Test set score : 0.9667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation Classification Models\n",
        "Evaluating the performance of a Machine learning model is one of the important steps while building an effective ML model. To evaluate the performance or quality of the model, different metrics are used, and these metrics are known as performance metrics or evaluation metrics."
      ],
      "metadata": {
        "id": "UVgT3jD4Nq3e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Confusion Matrix\n",
        "A confusion matrix is a tabular representation of prediction outcomes of any binary classifier, which is used to describe the performance of the classification model on a set of test data when true values are known."
      ],
      "metadata": {
        "id": "ubO1g7eZN0N-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confusion Matrix for DecisionTreeClassifier Without Parameters"
      ],
      "metadata": {
        "id": "OSozrNFFN9qV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "cm_des=DecisionTreeClassifier()\n",
        "\n",
        "cm_des.fit(x_train, y_train)\n",
        "y_pred_cm=cm_des.predict(x_test)\n",
        "y_true=y_test\n",
        "\n",
        "cm_des1=confusion_matrix(y_true, y_pred_cm)\n",
        "cm_des1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pbOEufg2OC59",
        "outputId": "15511165-5209-47b9-a5d7-f58f14fee97d"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 4,  0,  2,  0,  0],\n",
              "       [ 0,  4,  0,  0,  0],\n",
              "       [ 0,  0,  4,  0,  0],\n",
              "       [ 0,  0,  0, 19,  0],\n",
              "       [ 0,  0,  0,  0, 27]])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax=plt.subplots(figsize=(7, 5))\n",
        "sns.heatmap(cm_des1, annot=True, linewidths=0.5, linecolor='red', fmt='.0f', ax=ax)\n",
        "plt.xlabel('y_pred')\n",
        "plt.ylabel('y_true')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "S0LGvMxMOWHe",
        "outputId": "f43f4217-90c6-4aa8-c3e0-a0f2ecc2d0d2"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confusion Matrix For Decision Tree Classifier With 'gini'"
      ],
      "metadata": {
        "id": "CsP6ic5QOicO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm_des_gini=DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=0)\n",
        "\n",
        "cm_des_gini.fit(x_train, y_train)\n",
        "y_pred_cm=cm_des_gini.predict(x_test)\n",
        "y_true=y_test\n",
        "\n",
        "cm_des2=confusion_matrix(y_true, y_pred_cm)\n",
        "cm_des2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VeQK33BtOnaF",
        "outputId": "2673bfcc-a9d4-48b0-d6b9-3f8b63a1ac0b"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 4,  0,  2,  0,  0],\n",
              "       [ 0,  0,  0,  4,  0],\n",
              "       [ 0,  0,  4,  0,  0],\n",
              "       [ 0,  0,  0, 19,  0],\n",
              "       [ 0,  0,  0,  0, 27]])"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax=plt.subplots(figsize=(7, 5))\n",
        "sns.heatmap(cm_des2, annot=True, linewidths=0.5, linecolor='red', fmt='.0f', ax=ax)\n",
        "plt.xlabel('y_pred')\n",
        "plt.ylabel('y_true')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "-4dnIb9FO2pd",
        "outputId": "46dad145-b4fd-47a1-c4cb-6eeeb29dfb10"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Confusion Matrix For Random Forest Classifier With The Best Parameters"
      ],
      "metadata": {
        "id": "m9CduprBPFUe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cm_last_rfc=RandomForestClassifier(n_estimators=100, random_state=5)\n",
        "cm_last_rfc.fit(x_train, y_train)\n",
        "y_pred_cm=cm_last_rfc.predict(x_test)\n",
        "y_true=y_test\n",
        "cm_rfc=confusion_matrix(y_true, y_pred_cm)\n",
        "cm_rfc"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Di3I8GvLPEgd",
        "outputId": "b793ba36-fef6-4c65-d6a6-58d949f8daaa"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 4,  0,  2,  0,  0],\n",
              "       [ 0,  4,  0,  0,  0],\n",
              "       [ 0,  0,  4,  0,  0],\n",
              "       [ 0,  0,  0, 19,  0],\n",
              "       [ 0,  0,  0,  0, 27]])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f, ax=plt.subplots(figsize=(7, 5))\n",
        "sns.heatmap(cm_rfc, annot=True, linewidth=0.5, linecolor='red', fmt='.0f', ax=ax)\n",
        "plt.xlabel('y_pred')\n",
        "plt.ylabel('y_true')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "wh6mEYILPX1d",
        "outputId": "7457522f-2c5c-4428-a8f1-5b22ab621106"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}