ECG_Arrhythmia_Classification.ipynb

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      "gpuType": "T4"
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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "em4EGim5k7md"
      },
      "outputs": [],
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle\n",
        "\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "\n",
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "I2yBvGC5lCLk",
        "outputId": "a8dcc13e-baf3-46c7-aa40-98bddd35ec19"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.6.14)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi>=2023.7.22 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2024.6.2)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.4)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.4)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.7)\n"
          ]
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              "\n",
              "     <input type=\"file\" id=\"files-feb085e9-83ba-4b54-9720-8fd7f4a8e63c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-feb085e9-83ba-4b54-9720-8fd7f4a8e63c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
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              "    element.style[key] = styleAttributes[key];\n",
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              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
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              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
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              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
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              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
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              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d sadmansakib7/ecg-arrhythmia-classification-dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mk1UYDgUlLq9",
        "outputId": "0b546f83-d90f-4b39-ed0b-3c508ee6d276"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/sadmansakib7/ecg-arrhythmia-classification-dataset\n",
            "License(s): DbCL-1.0\n",
            "Downloading ecg-arrhythmia-classification-dataset.zip to /content\n",
            " 97% 121M/125M [00:01<00:00, 95.4MB/s] \n",
            "100% 125M/125M [00:01<00:00, 73.5MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip ecg-arrhythmia-classification-dataset.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dp3pAZx2lRF6",
        "outputId": "4c869623-4dc6-40ec-b7cb-0ceb7cc006b1"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  ecg-arrhythmia-classification-dataset.zip\n",
            "  inflating: INCART 2-lead Arrhythmia Database.csv  \n",
            "  inflating: MIT-BIH Arrhythmia Database.csv  \n",
            "  inflating: MIT-BIH Supraventricular Arrhythmia Database.csv  \n",
            "  inflating: Sudden Cardiac Death Holter Database.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df = pd.read_csv('/content/MIT-BIH Arrhythmia Database.csv')\n",
        "print(data_df.shape)\n",
        "data_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "kz6EYZZMlVPj",
        "outputId": "c5917171-f58a-44fa-d165-34453328e3cd"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(100689, 34)\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-782e1b95-ad42-4267-8fea-f6a1e1087500 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-782e1b95-ad42-4267-8fea-f6a1e1087500');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      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",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f026b357-c693-480d-b794-9717011423dc 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",
              "variable_name": "data_df"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_data = data_df.iloc[:, 2:]\n",
        "y_label = data_df[['type']]\n",
        "x_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "pBiVzTq-ltJy",
        "outputId": "efbe65ba-a263-434f-c54a-2f24ca76f4ba"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0_pre-RR  0_post-RR   0_pPeak   0_tPeak   0_rPeak   0_sPeak   0_qPeak  \\\n",
              "0        76      313.0  0.074347 -0.160548  1.036401 -0.285662 -0.026824   \n",
              "1       313      315.0 -0.052079 -0.264784  0.886597 -0.366298 -0.059710   \n",
              "2       315      321.0 -0.062151 -0.296983  0.991859 -0.410306 -0.065686   \n",
              "3       321      336.0 -0.063322 -0.281386  1.034903 -0.403880 -0.071750   \n",
              "4       336      344.0 -0.062915  1.046914  1.046408  1.046408 -0.074639   \n",
              "\n",
              "   0_qrs_interval  0_pq_interval  0_qt_interval  ...   1_qPeak  \\\n",
              "0              41             18             66  ...  0.025930   \n",
              "1              21              4             33  ... -0.042009   \n",
              "2              22              3             32  ...  0.009528   \n",
              "3              22              4             33  ... -0.020536   \n",
              "4              11              4             16  ...  0.016053   \n",
              "\n",
              "   1_qrs_interval  1_pq_interval  1_qt_interval  1_st_interval  1_qrs_morph0  \\\n",
              "0               2             18             22              2      0.025930   \n",
              "1              26             27             62              9     -0.042009   \n",
              "2               3              8             12              1      0.009528   \n",
              "3               6              9             16              1     -0.020536   \n",
              "4              16              5             31             10      0.016053   \n",
              "\n",
              "   1_qrs_morph1  1_qrs_morph2  1_qrs_morph3  1_qrs_morph4  \n",
              "0      0.025930      0.025930      0.025436      0.025436  \n",
              "1     -0.029498      0.005012      0.030892      0.002986  \n",
              "2      0.009528      0.008786      0.008786      0.008368  \n",
              "3     -0.020257     -0.018965     -0.016968     -0.014555  \n",
              "4      0.006742      0.002782     -0.007798     -0.051155  \n",
              "\n",
              "[5 rows x 32 columns]"
            ],
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              "    <div>\n",
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              "      <th></th>\n",
              "      <th>0_pre-RR</th>\n",
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              "      <th>0_pq_interval</th>\n",
              "      <th>0_qt_interval</th>\n",
              "      <th>...</th>\n",
              "      <th>1_qPeak</th>\n",
              "      <th>1_qrs_interval</th>\n",
              "      <th>1_pq_interval</th>\n",
              "      <th>1_qt_interval</th>\n",
              "      <th>1_st_interval</th>\n",
              "      <th>1_qrs_morph0</th>\n",
              "      <th>1_qrs_morph1</th>\n",
              "      <th>1_qrs_morph2</th>\n",
              "      <th>1_qrs_morph3</th>\n",
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              "      <td>41</td>\n",
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              "      <td>0.025930</td>\n",
              "      <td>0.025930</td>\n",
              "      <td>0.025436</td>\n",
              "      <td>0.025436</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>315.0</td>\n",
              "      <td>-0.052079</td>\n",
              "      <td>-0.264784</td>\n",
              "      <td>0.886597</td>\n",
              "      <td>-0.366298</td>\n",
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              "      <td>321.0</td>\n",
              "      <td>-0.062151</td>\n",
              "      <td>-0.296983</td>\n",
              "      <td>0.991859</td>\n",
              "      <td>-0.410306</td>\n",
              "      <td>-0.065686</td>\n",
              "      <td>22</td>\n",
              "      <td>3</td>\n",
              "      <td>32</td>\n",
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              "      <td>0.009528</td>\n",
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              "      <td>0.008786</td>\n",
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              "      <td>-0.281386</td>\n",
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              "      <td>-0.020257</td>\n",
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              "      <td>-0.016968</td>\n",
              "      <td>-0.014555</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>-0.051155</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 32 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea6c9c91-e196-4196-96c6-9f47abfcec54')\"\n",
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              "\n",
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              "  </svg>\n",
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              "\n",
              "  <style>\n",
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              "\n",
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              "\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ea6c9c91-e196-4196-96c6-9f47abfcec54');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\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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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\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",
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              "    (() => {\n",
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              "        document.querySelector('#df-63e92af4-8553-494e-9baf-64b7513a2c51 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "x_data"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_label.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZCU9cdiUlwkv",
        "outputId": "f2eec786-7b9e-47ea-d850-60566411eadd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type\n",
              "N       90083\n",
              "VEB      7009\n",
              "SVEB     2779\n",
              "F         803\n",
              "Q          15\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform multi-class labels into binary-class (arrhythmia and normal)\n",
        "y_label.replace(['VEB', 'SVEB', 'F', 'Q'], 'arrhythmia', inplace=True)\n",
        "y_label.replace(['N'], 'normal', inplace=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wkD-uY7_lyyC",
        "outputId": "3b49d26c-ee85-45e0-c941-4de4e2b1b07c"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-1bb2170c12bb>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  y_label.replace(['VEB', 'SVEB', 'F', 'Q'], 'arrhythmia', inplace=True)\n",
            "<ipython-input-9-1bb2170c12bb>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  y_label.replace(['N'], 'normal', inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_label.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jpDCJ1ZMl0jM",
        "outputId": "b73f3487-1d2b-4d3b-8aad-f9b6a3d1ec28"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type      \n",
              "normal        90083\n",
              "arrhythmia    10606\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_data, y_label, random_state=101)"
      ],
      "metadata": {
        "id": "sxOMr5xAl2ID"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import preprocessing\n",
        "\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "X_train_scaled = min_max_scaler.fit_transform(X_train)\n",
        "X_test_scaled = min_max_scaler.transform(X_test)\n",
        "\n",
        "print(min_max_scaler.scale_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_A98JKRl4Dq",
        "outputId": "5504223d-112b-409e-c231-2030a7e372eb"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.00047847 0.00047438 0.14497051 0.163272   0.15645161 0.13086489\n",
            " 0.14384207 0.00588235 0.00671141 0.00303951 0.00714286 0.14384207\n",
            " 0.18186031 0.16202895 0.16077164 0.17486125 0.00047847 0.00047438\n",
            " 0.13755979 0.10530541 0.12688961 0.10556911 0.10509129 0.00456621\n",
            " 0.00543478 0.002457   0.00531915 0.10509129 0.12377001 0.1410501\n",
            " 0.15095282 0.13532692]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_scaled"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYKf_sTJl5y2",
        "outputId": "fe12683b-490f-40c9-c827-c4fb33b12f1c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.15263158, 0.15512334, 0.44506625, ..., 0.56929435, 0.55969046,\n",
              "        0.56273247],\n",
              "       [0.08277512, 0.09250474, 0.45011298, ..., 0.59357774, 0.57069667,\n",
              "        0.56241605],\n",
              "       [0.13349282, 0.09250474, 0.45430416, ..., 0.60071605, 0.58812537,\n",
              "        0.58465607],\n",
              "       ...,\n",
              "       [0.10813397, 0.10721063, 0.44058918, ..., 0.61391008, 0.62145807,\n",
              "        0.64784558],\n",
              "       [0.22583732, 0.14848197, 0.44756388, ..., 0.62804987, 0.6482734 ,\n",
              "        0.68251227],\n",
              "       [0.13971292, 0.15227704, 0.47261668, ..., 0.63284376, 0.62555943,\n",
              "        0.58376543]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest Classifier"
      ],
      "metadata": {
        "id": "1w0cQW_6oTIv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier(random_state=101, n_estimators=150)\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "# training accuracy\n",
        "print('Accuracy for the train data', model.score(X_train_scaled, y_train))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ySkk_SRWl7No",
        "outputId": "ad2fe37a-9890-454a-eefa-ec15ae148aec"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-894164c032c4>:4: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  model.fit(X_train_scaled, y_train)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for the train data 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
        "\n",
        "print(\"*** Confusion Matrix ***\")\n",
        "print(metrics.confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUUcx8T3l9Gt",
        "outputId": "f0f0dc93-fd50-48b8-eb27-dba95d05f8cb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9901084495292576\n",
            "*** Confusion Matrix ***\n",
            "[[ 2389   192]\n",
            " [   57 22535]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy for the train data', model.score(X_train_scaled, y_train))\n",
        "print(\"Accuracy:\", metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "be42nUJjmLyE",
        "outputId": "bef13db2-71d4-45c8-eb7a-f931cc7df77e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for the train data 1.0\n",
            "Accuracy: 0.9901084495292576\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "CNN"
      ],
      "metadata": {
        "id": "8bLEpa4Enz2X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "# Load the dataset\n",
        "data_df = pd.read_csv('/content/MIT-BIH Arrhythmia Database.csv')\n",
        "\n",
        "# Transform multi-class labels into binary-class (arrhythmia and normal)\n",
        "data_df['type'].replace(['VEB', 'SVEB', 'F', 'Q'], 'arrhythmia', inplace=True)\n",
        "data_df['type'].replace(['N'], 'normal', inplace=True)\n",
        "\n",
        "# Split features and labels\n",
        "x_data = data_df.iloc[:, 2:]\n",
        "y_label = data_df[['type']]\n",
        "\n",
        "# Encode labels to integers\n",
        "label_encoder = LabelEncoder()\n",
        "y_label_encoded = label_encoder.fit_transform(y_label)\n",
        "\n",
        "# Split data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_data, y_label_encoded, random_state=101, stratify=y_label_encoded)\n",
        "\n",
        "# Normalize the features\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Reshape data to fit CNN input (samples, time steps, features)\n",
        "X_train_reshaped = X_train_scaled.reshape((X_train_scaled.shape[0], X_train_scaled.shape[1], 1))\n",
        "X_test_reshaped = X_test_scaled.reshape((X_test_scaled.shape[0], X_test_scaled.shape[1], 1))\n",
        "\n",
        "# Define the CNN model\n",
        "model = Sequential([\n",
        "    Conv1D(64, kernel_size=3, activation='relu', input_shape=(X_train_reshaped.shape[1], 1)),\n",
        "    MaxPooling1D(pool_size=2),\n",
        "    Conv1D(128, kernel_size=3, activation='relu'),\n",
        "    MaxPooling1D(pool_size=2),\n",
        "    Flatten(),\n",
        "    Dense(64, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(X_train_reshaped, y_train, epochs=20, batch_size=32, validation_split=0.2, verbose=1)\n",
        "\n",
        "# Evaluate the model\n",
        "y_pred = (model.predict(X_test_reshaped) > 0.5).astype(\"int32\")\n",
        "\n",
        "# Print accuracy\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "\n",
        "# Print confusion matrix\n",
        "print(\"*** Confusion Matrix ***\")\n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6dVoJMJ5mzK_",
        "outputId": "255475b1-1a18-4c10-d449-e58c725c2d35"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1888/1888 [==============================] - 15s 5ms/step - loss: 0.1984 - accuracy: 0.9345 - val_loss: 0.1262 - val_accuracy: 0.9574\n",
            "Epoch 2/20\n",
            "1888/1888 [==============================] - 9s 5ms/step - loss: 0.1192 - accuracy: 0.9583 - val_loss: 0.0956 - val_accuracy: 0.9643\n",
            "Epoch 3/20\n",
            "1888/1888 [==============================] - 9s 5ms/step - loss: 0.0976 - accuracy: 0.9662 - val_loss: 0.0799 - val_accuracy: 0.9727\n",
            "Epoch 4/20\n",
            "1888/1888 [==============================] - 9s 5ms/step - loss: 0.0844 - accuracy: 0.9713 - val_loss: 0.0814 - val_accuracy: 0.9713\n",
            "Epoch 5/20\n",
            "1888/1888 [==============================] - 8s 4ms/step - loss: 0.0762 - accuracy: 0.9743 - val_loss: 0.0665 - val_accuracy: 0.9770\n",
            "Epoch 6/20\n",
            "1888/1888 [==============================] - 10s 5ms/step - loss: 0.0725 - accuracy: 0.9761 - val_loss: 0.0652 - val_accuracy: 0.9764\n",
            "Epoch 7/20\n",
            "1888/1888 [==============================] - 9s 5ms/step - loss: 0.0684 - accuracy: 0.9777 - val_loss: 0.0609 - val_accuracy: 0.9777\n",
            "Epoch 8/20\n",
            "1888/1888 [==============================] - 8s 4ms/step - loss: 0.0644 - accuracy: 0.9785 - val_loss: 0.0622 - val_accuracy: 0.9780\n",
            "Epoch 9/20\n",
            "1888/1888 [==============================] - 9s 5ms/step - loss: 0.0609 - accuracy: 0.9796 - val_loss: 0.0572 - val_accuracy: 0.9789\n",
            "Epoch 10/20\n",
            "1888/1888 [==============================] - 8s 4ms/step - loss: 0.0603 - accuracy: 0.9804 - val_loss: 0.0549 - val_accuracy: 0.9798\n",
            "Epoch 11/20\n",
            "1888/1888 [==============================] - 9s 5ms/step - loss: 0.0577 - accuracy: 0.9808 - val_loss: 0.0531 - val_accuracy: 0.9813\n",
            "Epoch 12/20\n",
            "1888/1888 [==============================] - 9s 5ms/step - loss: 0.0555 - accuracy: 0.9815 - val_loss: 0.0504 - val_accuracy: 0.9825\n",
            "Epoch 13/20\n",
            "1888/1888 [==============================] - 8s 4ms/step - loss: 0.0542 - accuracy: 0.9823 - val_loss: 0.0591 - val_accuracy: 0.9805\n",
            "Epoch 14/20\n",
            "1888/1888 [==============================] - 10s 5ms/step - loss: 0.0526 - accuracy: 0.9828 - val_loss: 0.0492 - val_accuracy: 0.9831\n",
            "Epoch 15/20\n",
            "1888/1888 [==============================] - 8s 4ms/step - loss: 0.0514 - accuracy: 0.9829 - val_loss: 0.0536 - val_accuracy: 0.9819\n",
            "Epoch 16/20\n",
            "1888/1888 [==============================] - 9s 5ms/step - loss: 0.0500 - accuracy: 0.9831 - val_loss: 0.0470 - val_accuracy: 0.9845\n",
            "Epoch 17/20\n",
            "1888/1888 [==============================] - 9s 5ms/step - loss: 0.0493 - accuracy: 0.9842 - val_loss: 0.0561 - val_accuracy: 0.9819\n",
            "Epoch 18/20\n",
            "1888/1888 [==============================] - 11s 6ms/step - loss: 0.0483 - accuracy: 0.9841 - val_loss: 0.0545 - val_accuracy: 0.9819\n",
            "Epoch 19/20\n",
            "1888/1888 [==============================] - 9s 5ms/step - loss: 0.0461 - accuracy: 0.9847 - val_loss: 0.0515 - val_accuracy: 0.9818\n",
            "Epoch 20/20\n",
            "1888/1888 [==============================] - 10s 5ms/step - loss: 0.0451 - accuracy: 0.9852 - val_loss: 0.0443 - val_accuracy: 0.9856\n",
            "787/787 [==============================] - 1s 2ms/step\n",
            "Accuracy: 0.9851030866404481\n",
            "*** Confusion Matrix ***\n",
            "[[ 2396   256]\n",
            " [  119 22402]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Logistic Regression"
      ],
      "metadata": {
        "id": "-084KKcNn8gN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "# Load the dataset\n",
        "data_df = pd.read_csv('/content/MIT-BIH Arrhythmia Database.csv')\n",
        "\n",
        "# Transform multi-class labels into binary-class (arrhythmia and normal)\n",
        "data_df['type'].replace(['VEB', 'SVEB', 'F', 'Q'], 'arrhythmia', inplace=True)\n",
        "data_df['type'].replace(['N'], 'normal', inplace=True)\n",
        "\n",
        "# Split features and labels\n",
        "x_data = data_df.iloc[:, 2:]\n",
        "y_label = data_df[['type']]\n",
        "\n",
        "# Encode labels to integers\n",
        "label_encoder = LabelEncoder()\n",
        "y_label_encoded = label_encoder.fit_transform(y_label)\n",
        "\n",
        "# Split data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_data, y_label_encoded, random_state=101, stratify=y_label_encoded)\n",
        "\n",
        "# Normalize the features\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Define the Logistic Regression model\n",
        "model = LogisticRegression(random_state=101, max_iter=10000)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Predict on the test data\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "\n",
        "# Print accuracy\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "\n",
        "# Print confusion matrix\n",
        "print(\"*** Confusion Matrix ***\")\n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srmjsLebnLlb",
        "outputId": "e18e0b7b-f40f-472c-eb43-b67ba70a7c9d"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9464902872124896\n",
            "*** Confusion Matrix ***\n",
            "[[ 1539  1113]\n",
            " [  234 22287]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "VGG16"
      ],
      "metadata": {
        "id": "_S0h1swSoHom"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv1D, MaxPooling1D, Flatten, Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "# Load the dataset\n",
        "data_df = pd.read_csv('/content/MIT-BIH Arrhythmia Database.csv')\n",
        "\n",
        "# Transform multi-class labels into binary-class (arrhythmia and normal)\n",
        "data_df['type'].replace(['VEB', 'SVEB', 'F', 'Q'], 'arrhythmia', inplace=True)\n",
        "data_df['type'].replace(['N'], 'normal', inplace=True)\n",
        "\n",
        "# Split features and labels\n",
        "x_data = data_df.iloc[:, 2:]\n",
        "y_label = data_df[['type']]\n",
        "\n",
        "# Encode labels to integers\n",
        "label_encoder = LabelEncoder()\n",
        "y_label_encoded = label_encoder.fit_transform(y_label)\n",
        "\n",
        "# Split data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_data, y_label_encoded, random_state=101, stratify=y_label_encoded)\n",
        "\n",
        "# Normalize the features\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Reshape data to fit CNN input (samples, time steps, features)\n",
        "X_train_reshaped = X_train_scaled.reshape((X_train_scaled.shape[0], X_train_scaled.shape[1], 1))\n",
        "X_test_reshaped = X_test_scaled.reshape((X_test_scaled.shape[0], X_test_scaled.shape[1], 1))\n",
        "\n",
        "# Define the VGG16-like model for 1D data\n",
        "model = Sequential([\n",
        "    Conv1D(64, kernel_size=3, activation='relu', padding='same', input_shape=(X_train_reshaped.shape[1], 1)),\n",
        "    Conv1D(64, kernel_size=3, activation='relu', padding='same'),\n",
        "    MaxPooling1D(pool_size=2, strides=2),\n",
        "\n",
        "    Conv1D(128, kernel_size=3, activation='relu', padding='same'),\n",
        "    Conv1D(128, kernel_size=3, activation='relu', padding='same'),\n",
        "    MaxPooling1D(pool_size=2, strides=2),\n",
        "\n",
        "    Conv1D(256, kernel_size=3, activation='relu', padding='same'),\n",
        "    Conv1D(256, kernel_size=3, activation='relu', padding='same'),\n",
        "    Conv1D(256, kernel_size=3, activation='relu', padding='same'),\n",
        "    MaxPooling1D(pool_size=2, strides=2),\n",
        "\n",
        "    Conv1D(512, kernel_size=3, activation='relu', padding='same'),\n",
        "    Conv1D(512, kernel_size=3, activation='relu', padding='same'),\n",
        "    Conv1D(512, kernel_size=3, activation='relu', padding='same'),\n",
        "    MaxPooling1D(pool_size=2, strides=2),\n",
        "\n",
        "    Conv1D(512, kernel_size=3, activation='relu', padding='same'),\n",
        "    Conv1D(512, kernel_size=3, activation='relu', padding='same'),\n",
        "    Conv1D(512, kernel_size=3, activation='relu', padding='same'),\n",
        "    MaxPooling1D(pool_size=2, strides=2),\n",
        "\n",
        "    Flatten(),\n",
        "    Dense(4096, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(4096, activation='relu'),\n",
        "    Dropout(0.5),\n",
        "    Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(X_train_reshaped, y_train, epochs=20, batch_size=32, validation_split=0.2, verbose=1)\n",
        "\n",
        "# Evaluate the model\n",
        "y_pred = (model.predict(X_test_reshaped) > 0.5).astype(\"int32\")\n",
        "\n",
        "# Print accuracy\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "\n",
        "# Print confusion matrix\n",
        "print(\"*** Confusion Matrix ***\")\n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKHIONbNoCbv",
        "outputId": "eed331c4-83e0-44be-d035-b66adde2614f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1888/1888 [==============================] - 37s 17ms/step - loss: 0.1564 - accuracy: 0.9492 - val_loss: 0.0963 - val_accuracy: 0.9631\n",
            "Epoch 2/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0855 - accuracy: 0.9716 - val_loss: 0.0785 - val_accuracy: 0.9717\n",
            "Epoch 3/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0715 - accuracy: 0.9763 - val_loss: 0.0784 - val_accuracy: 0.9705\n",
            "Epoch 4/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0641 - accuracy: 0.9790 - val_loss: 0.0586 - val_accuracy: 0.9801\n",
            "Epoch 5/20\n",
            "1888/1888 [==============================] - 29s 15ms/step - loss: 0.0604 - accuracy: 0.9802 - val_loss: 0.0531 - val_accuracy: 0.9819\n",
            "Epoch 6/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0559 - accuracy: 0.9822 - val_loss: 0.0619 - val_accuracy: 0.9784\n",
            "Epoch 7/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0518 - accuracy: 0.9832 - val_loss: 0.0572 - val_accuracy: 0.9804\n",
            "Epoch 8/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0490 - accuracy: 0.9841 - val_loss: 0.0423 - val_accuracy: 0.9861\n",
            "Epoch 9/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0469 - accuracy: 0.9855 - val_loss: 0.0451 - val_accuracy: 0.9835\n",
            "Epoch 10/20\n",
            "1888/1888 [==============================] - 29s 15ms/step - loss: 0.0436 - accuracy: 0.9861 - val_loss: 0.0548 - val_accuracy: 0.9824\n",
            "Epoch 11/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0420 - accuracy: 0.9864 - val_loss: 0.0446 - val_accuracy: 0.9849\n",
            "Epoch 12/20\n",
            "1888/1888 [==============================] - 29s 15ms/step - loss: 0.0402 - accuracy: 0.9875 - val_loss: 0.0454 - val_accuracy: 0.9867\n",
            "Epoch 13/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0380 - accuracy: 0.9879 - val_loss: 0.0493 - val_accuracy: 0.9846\n",
            "Epoch 14/20\n",
            "1888/1888 [==============================] - 29s 15ms/step - loss: 0.0372 - accuracy: 0.9884 - val_loss: 0.0455 - val_accuracy: 0.9854\n",
            "Epoch 15/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0353 - accuracy: 0.9892 - val_loss: 0.0401 - val_accuracy: 0.9871\n",
            "Epoch 16/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0346 - accuracy: 0.9890 - val_loss: 0.0563 - val_accuracy: 0.9809\n",
            "Epoch 17/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0332 - accuracy: 0.9893 - val_loss: 0.0514 - val_accuracy: 0.9835\n",
            "Epoch 18/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0315 - accuracy: 0.9897 - val_loss: 0.0403 - val_accuracy: 0.9886\n",
            "Epoch 19/20\n",
            "1888/1888 [==============================] - 29s 16ms/step - loss: 0.0297 - accuracy: 0.9906 - val_loss: 0.0380 - val_accuracy: 0.9866\n",
            "Epoch 20/20\n",
            "1888/1888 [==============================] - 30s 16ms/step - loss: 0.0291 - accuracy: 0.9908 - val_loss: 0.0374 - val_accuracy: 0.9868\n",
            "787/787 [==============================] - 3s 4ms/step\n",
            "Accuracy: 0.986215389504628\n",
            "*** Confusion Matrix ***\n",
            "[[ 2413   239]\n",
            " [  108 22413]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "ResNet"
      ],
      "metadata": {
        "id": "hLixB5rFoYYP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Conv1D, BatchNormalization, Activation, Add, Flatten, Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "# Load the dataset\n",
        "data_df = pd.read_csv('/content/MIT-BIH Arrhythmia Database.csv')\n",
        "\n",
        "# Transform multi-class labels into binary-class (arrhythmia and normal)\n",
        "data_df['type'].replace(['VEB', 'SVEB', 'F', 'Q'], 'arrhythmia', inplace=True)\n",
        "data_df['type'].replace(['N'], 'normal', inplace=True)\n",
        "\n",
        "# Split features and labels\n",
        "x_data = data_df.iloc[:, 2:]\n",
        "y_label = data_df[['type']]\n",
        "\n",
        "# Encode labels to integers\n",
        "label_encoder = LabelEncoder()\n",
        "y_label_encoded = label_encoder.fit_transform(y_label)\n",
        "\n",
        "# Split data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_data, y_label_encoded, random_state=101, stratify=y_label_encoded)\n",
        "\n",
        "# Normalize the features\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Reshape data to fit CNN input (samples, time steps, features)\n",
        "X_train_reshaped = X_train_scaled.reshape((X_train_scaled.shape[0], X_train_scaled.shape[1], 1))\n",
        "X_test_reshaped = X_test_scaled.reshape((X_test_scaled.shape[0], X_test_scaled.shape[1], 1))\n",
        "\n",
        "# Define the ResNet block\n",
        "def resnet_block(input_tensor, filters, kernel_size=3, strides=1, activation='relu'):\n",
        "    x = Conv1D(filters, kernel_size, strides=strides, padding='same')(input_tensor)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation(activation)(x)\n",
        "\n",
        "    x = Conv1D(filters, kernel_size, padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "\n",
        "    x = Add()([x, input_tensor])\n",
        "    x = Activation(activation)(x)\n",
        "    return x\n",
        "\n",
        "# Define the ResNet-like model for 1D data\n",
        "input_shape = (X_train_reshaped.shape[1], 1)\n",
        "input_layer = Input(shape=input_shape)\n",
        "x = Conv1D(64, 7, strides=2, padding='same')(input_layer)\n",
        "x = BatchNormalization()(x)\n",
        "x = Activation('relu')(x)\n",
        "x = MaxPooling1D(pool_size=3, strides=2, padding='same')(x)\n",
        "\n",
        "# Add ResNet blocks\n",
        "num_res_blocks = 3\n",
        "for _ in range(num_res_blocks):\n",
        "    x = resnet_block(x, filters=64)\n",
        "\n",
        "x = Flatten()(x)\n",
        "x = Dense(128, activation='relu')(x)\n",
        "x = Dense(1, activation='sigmoid')(x)\n",
        "\n",
        "model = Model(inputs=input_layer, outputs=x)\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(X_train_reshaped, y_train, epochs=20, batch_size=32, validation_split=0.2, verbose=1)\n",
        "\n",
        "# Evaluate the model\n",
        "y_pred = (model.predict(X_test_reshaped) > 0.5).astype(\"int32\")\n",
        "\n",
        "# Print accuracy\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "\n",
        "# Print confusion matrix\n",
        "print(\"*** Confusion Matrix ***\")\n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nOr01t6HoEpB",
        "outputId": "e78886c4-50e3-4e19-a899-a429d39df46f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1888/1888 [==============================] - 30s 12ms/step - loss: 0.0893 - accuracy: 0.9711 - val_loss: 0.0594 - val_accuracy: 0.9805\n",
            "Epoch 2/20\n",
            "1888/1888 [==============================] - 23s 12ms/step - loss: 0.0551 - accuracy: 0.9827 - val_loss: 0.0564 - val_accuracy: 0.9821\n",
            "Epoch 3/20\n",
            "1888/1888 [==============================] - 22s 12ms/step - loss: 0.0474 - accuracy: 0.9845 - val_loss: 0.0572 - val_accuracy: 0.9812\n",
            "Epoch 4/20\n",
            "1888/1888 [==============================] - 23s 12ms/step - loss: 0.0408 - accuracy: 0.9868 - val_loss: 0.0473 - val_accuracy: 0.9840\n",
            "Epoch 5/20\n",
            "1888/1888 [==============================] - 22s 12ms/step - loss: 0.0376 - accuracy: 0.9879 - val_loss: 0.0407 - val_accuracy: 0.9858\n",
            "Epoch 6/20\n",
            "1888/1888 [==============================] - 23s 12ms/step - loss: 0.0330 - accuracy: 0.9889 - val_loss: 0.0532 - val_accuracy: 0.9827\n",
            "Epoch 7/20\n",
            "1888/1888 [==============================] - 23s 12ms/step - loss: 0.0304 - accuracy: 0.9899 - val_loss: 0.0432 - val_accuracy: 0.9870\n",
            "Epoch 8/20\n",
            "1888/1888 [==============================] - 22s 12ms/step - loss: 0.0284 - accuracy: 0.9903 - val_loss: 0.0415 - val_accuracy: 0.9876\n",
            "Epoch 9/20\n",
            "1888/1888 [==============================] - 23s 12ms/step - loss: 0.0276 - accuracy: 0.9909 - val_loss: 0.0452 - val_accuracy: 0.9860\n",
            "Epoch 10/20\n",
            "1888/1888 [==============================] - 22s 12ms/step - loss: 0.0259 - accuracy: 0.9917 - val_loss: 0.0524 - val_accuracy: 0.9835\n",
            "Epoch 11/20\n",
            "1888/1888 [==============================] - 23s 12ms/step - loss: 0.0241 - accuracy: 0.9920 - val_loss: 0.0489 - val_accuracy: 0.9844\n",
            "Epoch 12/20\n",
            "1888/1888 [==============================] - 22s 11ms/step - loss: 0.0237 - accuracy: 0.9921 - val_loss: 0.0327 - val_accuracy: 0.9901\n",
            "Epoch 13/20\n",
            "1888/1888 [==============================] - 23s 12ms/step - loss: 0.0220 - accuracy: 0.9925 - val_loss: 0.0403 - val_accuracy: 0.9886\n",
            "Epoch 14/20\n",
            "1888/1888 [==============================] - 23s 12ms/step - loss: 0.0207 - accuracy: 0.9930 - val_loss: 0.0593 - val_accuracy: 0.9811\n",
            "Epoch 15/20\n",
            "1888/1888 [==============================] - 22s 12ms/step - loss: 0.0202 - accuracy: 0.9932 - val_loss: 0.0340 - val_accuracy: 0.9890\n",
            "Epoch 16/20\n",
            "1888/1888 [==============================] - 24s 12ms/step - loss: 0.0192 - accuracy: 0.9938 - val_loss: 0.0408 - val_accuracy: 0.9878\n",
            "Epoch 17/20\n",
            "1888/1888 [==============================] - 22s 12ms/step - loss: 0.0180 - accuracy: 0.9940 - val_loss: 0.0308 - val_accuracy: 0.9905\n",
            "Epoch 18/20\n",
            "1888/1888 [==============================] - 24s 13ms/step - loss: 0.0171 - accuracy: 0.9942 - val_loss: 0.0318 - val_accuracy: 0.9905\n",
            "Epoch 19/20\n",
            "1888/1888 [==============================] - 22s 12ms/step - loss: 0.0174 - accuracy: 0.9946 - val_loss: 0.0308 - val_accuracy: 0.9903\n",
            "Epoch 20/20\n",
            "1888/1888 [==============================] - 23s 12ms/step - loss: 0.0155 - accuracy: 0.9947 - val_loss: 0.0338 - val_accuracy: 0.9893\n",
            "787/787 [==============================] - 3s 3ms/step\n",
            "Accuracy: 0.9891153219719541\n",
            "*** Confusion Matrix ***\n",
            "[[ 2536   116]\n",
            " [  158 22363]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SVM"
      ],
      "metadata": {
        "id": "PkJrW30sqhY-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "\n",
        "# Load the dataset\n",
        "data_df = pd.read_csv('/content/MIT-BIH Arrhythmia Database.csv')\n",
        "\n",
        "# Transform multi-class labels into binary-class (arrhythmia and normal)\n",
        "data_df['type'].replace(['VEB', 'SVEB', 'F', 'Q'], 'arrhythmia', inplace=True)\n",
        "data_df['type'].replace(['N'], 'normal', inplace=True)\n",
        "\n",
        "# Split features and labels\n",
        "x_data = data_df.iloc[:, 2:]\n",
        "y_label = data_df[['type']]\n",
        "\n",
        "# Encode labels to integers\n",
        "label_encoder = LabelEncoder()\n",
        "y_label_encoded = label_encoder.fit_transform(y_label)\n",
        "\n",
        "# Split data into training and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(x_data, y_label_encoded, random_state=101, stratify=y_label_encoded)\n",
        "\n",
        "# Normalize the features\n",
        "scaler = MinMaxScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Define the SVM model\n",
        "model = SVC(kernel='linear', C=1, random_state=101)\n",
        "\n",
        "# Train the model\n",
        "model.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Predict on the test data\n",
        "y_pred = model.predict(X_test_scaled)\n",
        "\n",
        "# Print accuracy\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))\n",
        "\n",
        "# Print confusion matrix\n",
        "print(\"*** Confusion Matrix ***\")\n",
        "print(confusion_matrix(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUT3j_OepcBq",
        "outputId": "ea54a4da-ef94-4322-dfb4-cc60313e060e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/preprocessing/_label.py:116: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9478409406904222\n",
            "*** Confusion Matrix ***\n",
            "[[ 1624  1028]\n",
            " [  285 22236]]\n"
          ]
        }
      ]
    }
  ]
}