coronaVirusClassifier.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/PritomKarmokar/coronaVirusClassifier/blob/main/coronaVirusClassifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "### Preprocessing Phase"
      ],
      "metadata": {
        "id": "LldrgZFDb2As"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Importing necessary libaries"
      ],
      "metadata": {
        "id": "QfO_IEPTcVSO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "ZxvyZ-BwcZtK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Function for converting 'fasta ' files into csv\n",
        "def convert_fasta_to_csv(fasta_file, csv_file):\n",
        "    # Step 1: Load the FASTA file\n",
        "    with open(fasta_file, 'r') as f:\n",
        "        fasta_content = f.readlines()\n",
        "\n",
        "    # Step 2: Parse the FASTA data\n",
        "    sequences = []\n",
        "    current_sequence = ''\n",
        "    current_id = ''\n",
        "    for line in fasta_content:\n",
        "        if line.startswith('>'):\n",
        "            if current_sequence:\n",
        "                sequences.append((current_id, current_sequence))\n",
        "            current_id = line[1:].strip()\n",
        "            current_sequence = ''\n",
        "        else:\n",
        "            current_sequence += line.strip()\n",
        "    if current_sequence:\n",
        "        sequences.append((current_id, current_sequence))\n",
        "\n",
        "    # Step 3: Create a CSV file\n",
        "    with open(csv_file, 'w', newline='') as f:\n",
        "        writer = csv.writer(f)\n",
        "\n",
        "        # Step 4: Write headers\n",
        "        writer.writerow(['Sequence ID', 'Sequence'])\n",
        "\n",
        "        # Step 5: Write data rows\n",
        "        for sequence in sequences:\n",
        "            writer.writerow(sequence)\n",
        "\n",
        "    print(f'Conversion complete. CSV file saved as {csv_file}.')"
      ],
      "metadata": {
        "id": "esu_SHxrccdG"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "### function for counting no. of sequences presented in the file\n",
        "def count_sequences(filename):\n",
        "    count = 0\n",
        "    with open(filename, \"r\") as file:\n",
        "        for line in file:\n",
        "            if line.startswith(\">\"):\n",
        "                count += 1\n",
        "    return count"
      ],
      "metadata": {
        "id": "zjLkecQpc0cl"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Function for finding maximum length from the sequence\n",
        "def maxSeq_length(data):\n",
        "    res = max([len(seq) for seq in data])\n",
        "    return res"
      ],
      "metadata": {
        "id": "IcMqCxQ8c6Oo"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### Function for finding minimum length from the sequence\n",
        "def minSeq_length(data):\n",
        "    res = min([len(seq) for seq in data])\n",
        "    return res"
      ],
      "metadata": {
        "id": "GrfLHpybc8wW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading the data"
      ],
      "metadata": {
        "id": "sGydE_kmde7s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "i_pdg07jc-UU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "drive.mount('/content/drive')"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "6YuvSaFOddeM",
        "outputId": "96ffe31f-d24e-4ee9-d361-1223d30594a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datapath = '/content/drive/MyDrive/drive_for_data/covid_data/coronavirus/ndgc/'\n",
        "print(datapath)\n",
        "files = os.listdir(datapath)\n",
        "files"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z6TdqvSQdiX6",
        "outputId": "e8c40611-1752-485c-ad75-8e0150de1a43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/drive_for_data/covid_data/coronavirus/ndgc/\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['SARSr-COV.fasta',\n",
              " 'SARS-CoV2.fasta',\n",
              " 'HCoV-OC43.fasta',\n",
              " 'HCoV-229E.fasta',\n",
              " 'HCoV-NL63.fasta',\n",
              " 'HCoV-HKU1.fasta',\n",
              " 'mers_sequence.fasta']"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Working with SARS-CoV-2 genome sequences"
      ],
      "metadata": {
        "id": "VVqOpSp8eDVD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "convert_fasta_to_csv(datapath + 'SARS-CoV2.fasta', 'sars-cov2.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cuLQvNQeQLm",
        "outputId": "423b8117-7c88-4387-f53e-43bf501bc76d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conversion complete. CSV file saved as sars-cov2.csv.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_sarsCov2 = pd.read_csv('sars-cov2.csv')\n",
        "df_sarsCov2['strain_name'] = 'sars-cov2'\n",
        "print(f\"shape of sarsCov2 dataframe = {df_sarsCov2.shape}\")"
      ],
      "metadata": {
        "id": "49U_JlXDeVkg",
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          "base_uri": "https://localhost:8080/"
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of sarsCov2 dataframe = (400, 3)\n"
          ]
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "df_sarsCov2.head(2)"
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              "                                         Sequence ID  \\\n",
              "0  NC_045512.2 |Severe acute respiratory syndrome...   \n",
              "1  OR177857.1 |Severe acute respiratory syndrome ...   \n",
              "\n",
              "                                            Sequence strain_name  \n",
              "0  ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...   sars-cov2  \n",
              "1  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2  "
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    {
      "cell_type": "code",
      "source": [
        "## finding unique values\n",
        "df_sarsCov2['Sequence'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3fuQiVYee47V",
        "outputId": "6f62dea8-9034-4d8c-9ff3-0532b1b7cfb2"
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      "source": [
        "# Removing duplicate rows based on 'Sequence' column\n",
        "df_sarsCov2 = df_sarsCov2.drop_duplicates(subset='Sequence',ignore_index=True)\n",
        "df_sarsCov2"
      ],
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              "                                           Sequence ID  \\\n",
              "0    NC_045512.2 |Severe acute respiratory syndrome...   \n",
              "1    OR177857.1 |Severe acute respiratory syndrome ...   \n",
              "2    OR177858.1 |Severe acute respiratory syndrome ...   \n",
              "3    OR177859.1 |Severe acute respiratory syndrome ...   \n",
              "4    OR177860.1 |Severe acute respiratory syndrome ...   \n",
              "..                                                 ...   \n",
              "392  OR150936.1 |Severe acute respiratory syndrome ...   \n",
              "393  OR150937.1 |Severe acute respiratory syndrome ...   \n",
              "394  OR150938.1 |Severe acute respiratory syndrome ...   \n",
              "395  OR150939.1 |Severe acute respiratory syndrome ...   \n",
              "396  OR150940.1 |Severe acute respiratory syndrome ...   \n",
              "\n",
              "                                              Sequence strain_name  \n",
              "0    ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...   sars-cov2  \n",
              "1    TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2  \n",
              "2    TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2  \n",
              "3    TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2  \n",
              "4    TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2  \n",
              "..                                                 ...         ...  \n",
              "392  CANNCCAACCAACTTTCGATCTCTTGTAGATCTGTTCTCTAAACGA...   sars-cov2  \n",
              "393  CCAACCAACTTTCGATCTCTTGTAGATCTGTTCTCTAAACGAACTT...   sars-cov2  \n",
              "394  TACCTTCCTAGGTAACAAACCAACCAACTTTCGATCTCTTGTAGAT...   sars-cov2  \n",
              "395  CTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGT...   sars-cov2  \n",
              "396  CCAACCAACTTTCGATCTCTTGTAGATCTGTTCTCTAAACGAACTT...   sars-cov2  \n",
              "\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_045512.2 |Severe acute respiratory syndrome...</td>\n",
              "      <td>ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...</td>\n",
              "      <td>sars-cov2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OR177857.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OR177858.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OR177859.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OR177860.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392</th>\n",
              "      <td>OR150936.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>CANNCCAACCAACTTTCGATCTCTTGTAGATCTGTTCTCTAAACGA...</td>\n",
              "      <td>sars-cov2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>OR150937.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>CCAACCAACTTTCGATCTCTTGTAGATCTGTTCTCTAAACGAACTT...</td>\n",
              "      <td>sars-cov2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>394</th>\n",
              "      <td>OR150938.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>TACCTTCCTAGGTAACAAACCAACCAACTTTCGATCTCTTGTAGAT...</td>\n",
              "      <td>sars-cov2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>OR150939.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>CTTGTAGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGT...</td>\n",
              "      <td>sars-cov2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>OR150940.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>CCAACCAACTTTCGATCTCTTGTAGATCTGTTCTCTAAACGAACTT...</td>\n",
              "      <td>sars-cov2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>397 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9373c275-7665-48b5-bf11-d38cc1779d15')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-556643e0-c243-424e-80ea-2c45947c1f59\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-556643e0-c243-424e-80ea-2c45947c1f59')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-556643e0-c243-424e-80ea-2c45947c1f59 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9373c275-7665-48b5-bf11-d38cc1779d15 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-9373c275-7665-48b5-bf11-d38cc1779d15');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### After removing duplicate vaules\n",
        "print(f\"shape of sarsCov2 dataframe = {df_sarsCov2.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sqdLSOYCe-sm",
        "outputId": "e65f3ac7-8b37-4c89-bf20-4b15d5ff2617"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of sarsCov2 dataframe = (397, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_sarsCov2 = maxSeq_length(df_sarsCov2['Sequence'])\n",
        "max_len_sarsCov2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZzTcmf1fBAP",
        "outputId": "cc6157b7-1aa7-49db-f2e7-34beeab668dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29903"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_len_sarsCov2 = minSeq_length(df_sarsCov2['Sequence'])\n",
        "min_len_sarsCov2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WBqcnWqqfCqc",
        "outputId": "cd27d0a9-8c66-484c-84d8-2608ac876bec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29319"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Labeling 'sars-Cov2' as '0'\n",
        "df_sarsCov2['label'] = 0\n",
        "df_sarsCov2.head(2)"
      ],
      "metadata": {
        "id": "CnhOjzQXfh0i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "8662c4c8-3205-45c7-a201-fd72ccc6bccf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_045512.2 |Severe acute respiratory syndrome...   \n",
              "1  OR177857.1 |Severe acute respiratory syndrome ...   \n",
              "\n",
              "                                            Sequence strain_name  label  \n",
              "0  ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...   sars-cov2      0  \n",
              "1  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-04f2d80b-7e1a-4ea4-b88f-a1c499077eda\">\n",
              "    <div 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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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_045512.2 |Severe acute respiratory syndrome...</td>\n",
              "      <td>ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OR177857.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-04f2d80b-7e1a-4ea4-b88f-a1c499077eda')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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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              "      </button>\n",
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              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-58a33af6-80ea-40f9-a89f-b081575a9faa')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-58a33af6-80ea-40f9-a89f-b081575a9faa button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-04f2d80b-7e1a-4ea4-b88f-a1c499077eda 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-04f2d80b-7e1a-4ea4-b88f-a1c499077eda');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Working with MERS-CoV Genome Sequences"
      ],
      "metadata": {
        "id": "Qmmlwd90fodG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "convert_fasta_to_csv(datapath + 'mers_sequence.fasta', 'mers-cov.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzJILdkff6je",
        "outputId": "ed4eefd2-4d52-4584-f427-a2a7c1198f61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conversion complete. CSV file saved as mers-cov.csv.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_mersCov = pd.read_csv('mers-cov.csv')\n",
        "df_mersCov['strain_name'] = 'mers-cov'\n",
        "df_mersCov.shape"
      ],
      "metadata": {
        "id": "jGlEXXh7f_EC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9f7871f-703e-4d31-8708-5d6abfa18295"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1345, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## finding unique values\n",
        "df_mersCov['Sequence'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2AnFWfT4gh8c",
        "outputId": "027fefb9-0ce6-4b15-9093-dadca9539306"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1002"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicate rows based on 'Sequence' column\n",
        "df_mersCov = df_mersCov.drop_duplicates(subset='Sequence',ignore_index=True)\n",
        "df_mersCov.shape"
      ],
      "metadata": {
        "id": "FpagPpgXgj_f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24f1553d-8ec4-4046-954b-15b363b6107d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1002, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_mersCov.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "dvD37YFcgn74",
        "outputId": "4c988a8e-c53f-45d0-a257-3cff0b6be91a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_038294 |Betacoronavirus England 1| complete...   \n",
              "1  NC_019843 |Middle East respiratory syndrome co...   \n",
              "2  MG987420 |Middle East respiratory syndrome-rel...   \n",
              "3  MG987421 |Middle East respiratory syndrome-rel...   \n",
              "4  MN541264 |Middle East respiratory syndrome-rel...   \n",
              "\n",
              "                                            Sequence strain_name  \n",
              "0  ATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGC...    mers-cov  \n",
              "1  GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...    mers-cov  \n",
              "2  GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...    mers-cov  \n",
              "3  GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...    mers-cov  \n",
              "4  TGTTGATTGTAAACAGTACGTTTGCAATGGTTTCCAGAAGTGTGAG...    mers-cov  "
            ],
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              "\n",
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              "      <td>NC_038294 |Betacoronavirus England 1| complete...</td>\n",
              "      <td>ATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGC...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NC_019843 |Middle East respiratory syndrome co...</td>\n",
              "      <td>GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MG987420 |Middle East respiratory syndrome-rel...</td>\n",
              "      <td>GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MG987421 |Middle East respiratory syndrome-rel...</td>\n",
              "      <td>GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MN541264 |Middle East respiratory syndrome-rel...</td>\n",
              "      <td>TGTTGATTGTAAACAGTACGTTTGCAATGGTTTCCAGAAGTGTGAG...</td>\n",
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              "</style>\n",
              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
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              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_mersCov = maxSeq_length(df_mersCov['Sequence'])\n",
        "max_len_mersCov"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K2Xce-hNgps6",
        "outputId": "96e240d8-cdd8-44ff-c9db-531e35de9660"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30484"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_len_mersCov = minSeq_length(df_mersCov['Sequence'])\n",
        "min_len_mersCov"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfaLBXttgrfY",
        "outputId": "5701e7fb-6121-4c39-a1c0-58c65c83e270"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "110"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### containing all the 'sequences' whose length is greater than '25000'\n",
        "df_mersCov = df_mersCov[(df_mersCov['Sequence'].str.len() > 25000)]\n",
        "print(df_mersCov.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVPB8VUggtQZ",
        "outputId": "f487de05-73fe-434b-d32f-d5f76937ad9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(537, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_mersCov"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ZsqFoXUmgvJJ",
        "outputId": "10e0228c-80f2-4e7c-e121-1275080f0f1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            Sequence ID  \\\n",
              "0     NC_038294 |Betacoronavirus England 1| complete...   \n",
              "1     NC_019843 |Middle East respiratory syndrome co...   \n",
              "2     MG987420 |Middle East respiratory syndrome-rel...   \n",
              "3     MG987421 |Middle East respiratory syndrome-rel...   \n",
              "30    MN723544 |Middle East respiratory syndrome-rel...   \n",
              "...                                                 ...   \n",
              "997   KF186565 |Middle East respiratory syndrome cor...   \n",
              "998   KF192507 |Middle East respiratory syndrome cor...   \n",
              "999   KF186564 |Middle East respiratory syndrome cor...   \n",
              "1000  KC776174 |Human betacoronavirus 2c Jordan-N3/2...   \n",
              "1001  KC667074 |Human betacoronavirus 2c England-Qat...   \n",
              "\n",
              "                                               Sequence strain_name  \n",
              "0     ATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGC...    mers-cov  \n",
              "1     GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...    mers-cov  \n",
              "2     GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...    mers-cov  \n",
              "3     GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...    mers-cov  \n",
              "30    GATTTAAGTGAATAGCTTGGCTATCTCACTTTCCCTCGTTCTCTTG...    mers-cov  \n",
              "...                                                 ...         ...  \n",
              "997   AACTTTGATTTTAACGAACTTAAATAAAAGCCCTGTTGTTTAGCGT...    mers-cov  \n",
              "998   AAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGCAGAA...    mers-cov  \n",
              "999   CGTTCTCCTGCAGAACTTTGATTTTAACGAACTTAAATAAAAGCCC...    mers-cov  \n",
              "1000  CAGAACTTTGATTTTAACGAACTTAAATAAAAGCCCTGTTGTTTAG...    mers-cov  \n",
              "1001  GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...    mers-cov  \n",
              "\n",
              "[537 rows x 3 columns]"
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              "      <th>1</th>\n",
              "      <td>NC_019843 |Middle East respiratory syndrome co...</td>\n",
              "      <td>GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...</td>\n",
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              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>MG987420 |Middle East respiratory syndrome-rel...</td>\n",
              "      <td>GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...</td>\n",
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              "      <th>3</th>\n",
              "      <td>MG987421 |Middle East respiratory syndrome-rel...</td>\n",
              "      <td>GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>MN723544 |Middle East respiratory syndrome-rel...</td>\n",
              "      <td>GATTTAAGTGAATAGCTTGGCTATCTCACTTTCCCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>KF186565 |Middle East respiratory syndrome cor...</td>\n",
              "      <td>AACTTTGATTTTAACGAACTTAAATAAAAGCCCTGTTGTTTAGCGT...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>KF192507 |Middle East respiratory syndrome cor...</td>\n",
              "      <td>AAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGCAGAA...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>KF186564 |Middle East respiratory syndrome cor...</td>\n",
              "      <td>CGTTCTCCTGCAGAACTTTGATTTTAACGAACTTAAATAAAAGCCC...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1000</th>\n",
              "      <td>KC776174 |Human betacoronavirus 2c Jordan-N3/2...</td>\n",
              "      <td>CAGAACTTTGATTTTAACGAACTTAAATAAAAGCCCTGTTGTTTAG...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1001</th>\n",
              "      <td>KC667074 |Human betacoronavirus 2c England-Qat...</td>\n",
              "      <td>GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>537 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c9e499b-847d-44b5-b3cb-5092569ad4ff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-b38f1dd0-4703-4ae8-9ce4-e434c6d6def4\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b38f1dd0-4703-4ae8-9ce4-e434c6d6def4')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-b38f1dd0-4703-4ae8-9ce4-e434c6d6def4 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5c9e499b-847d-44b5-b3cb-5092569ad4ff 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-5c9e499b-847d-44b5-b3cb-5092569ad4ff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_mersCov = df_mersCov.reset_index(drop=True)\n",
        "df_mersCov"
      ],
      "metadata": {
        "id": "5uRlD4QFgxRj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "a5c8e08c-e007-40fb-b82b-04784dff0860"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           Sequence ID  \\\n",
              "0    NC_038294 |Betacoronavirus England 1| complete...   \n",
              "1    NC_019843 |Middle East respiratory syndrome co...   \n",
              "2    MG987420 |Middle East respiratory syndrome-rel...   \n",
              "3    MG987421 |Middle East respiratory syndrome-rel...   \n",
              "4    MN723544 |Middle East respiratory syndrome-rel...   \n",
              "..                                                 ...   \n",
              "532  KF186565 |Middle East respiratory syndrome cor...   \n",
              "533  KF192507 |Middle East respiratory syndrome cor...   \n",
              "534  KF186564 |Middle East respiratory syndrome cor...   \n",
              "535  KC776174 |Human betacoronavirus 2c Jordan-N3/2...   \n",
              "536  KC667074 |Human betacoronavirus 2c England-Qat...   \n",
              "\n",
              "                                              Sequence strain_name  \n",
              "0    ATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGC...    mers-cov  \n",
              "1    GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...    mers-cov  \n",
              "2    GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...    mers-cov  \n",
              "3    GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...    mers-cov  \n",
              "4    GATTTAAGTGAATAGCTTGGCTATCTCACTTTCCCTCGTTCTCTTG...    mers-cov  \n",
              "..                                                 ...         ...  \n",
              "532  AACTTTGATTTTAACGAACTTAAATAAAAGCCCTGTTGTTTAGCGT...    mers-cov  \n",
              "533  AAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGCAGAA...    mers-cov  \n",
              "534  CGTTCTCCTGCAGAACTTTGATTTTAACGAACTTAAATAAAAGCCC...    mers-cov  \n",
              "535  CAGAACTTTGATTTTAACGAACTTAAATAAAAGCCCTGTTGTTTAG...    mers-cov  \n",
              "536  GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...    mers-cov  \n",
              "\n",
              "[537 rows x 3 columns]"
            ],
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              "      <th>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_038294 |Betacoronavirus England 1| complete...</td>\n",
              "      <td>ATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGC...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NC_019843 |Middle East respiratory syndrome co...</td>\n",
              "      <td>GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MG987420 |Middle East respiratory syndrome-rel...</td>\n",
              "      <td>GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MG987421 |Middle East respiratory syndrome-rel...</td>\n",
              "      <td>GATTTAAGTGAATAGCCTAGCTATCTCACCCCCTCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MN723544 |Middle East respiratory syndrome-rel...</td>\n",
              "      <td>GATTTAAGTGAATAGCTTGGCTATCTCACTTTCCCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>532</th>\n",
              "      <td>KF186565 |Middle East respiratory syndrome cor...</td>\n",
              "      <td>AACTTTGATTTTAACGAACTTAAATAAAAGCCCTGTTGTTTAGCGT...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>533</th>\n",
              "      <td>KF192507 |Middle East respiratory syndrome cor...</td>\n",
              "      <td>AAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGCAGAA...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>534</th>\n",
              "      <td>KF186564 |Middle East respiratory syndrome cor...</td>\n",
              "      <td>CGTTCTCCTGCAGAACTTTGATTTTAACGAACTTAAATAAAAGCCC...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>535</th>\n",
              "      <td>KC776174 |Human betacoronavirus 2c Jordan-N3/2...</td>\n",
              "      <td>CAGAACTTTGATTTTAACGAACTTAAATAAAAGCCCTGTTGTTTAG...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>536</th>\n",
              "      <td>KC667074 |Human betacoronavirus 2c England-Qat...</td>\n",
              "      <td>GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>537 rows × 3 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dbb97bc2-08dd-44b1-9ee8-73736cb1d87b')\"\n",
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              "\n",
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              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Labeling 'MERS-CoV' as 1\n",
        "df_mersCov['label'] = 1\n",
        "df_mersCov.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "F0jpq8H4g19-",
        "outputId": "eae7a7e0-e10a-476d-9d32-913d532de23d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_038294 |Betacoronavirus England 1| complete...   \n",
              "1  NC_019843 |Middle East respiratory syndrome co...   \n",
              "\n",
              "                                            Sequence strain_name  label  \n",
              "0  ATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGC...    mers-cov      1  \n",
              "1  GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...    mers-cov      1  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-d90e05d1-337d-4963-8f97-f5622a8a6bfe\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_038294 |Betacoronavirus England 1| complete...</td>\n",
              "      <td>ATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTGC...</td>\n",
              "      <td>mers-cov</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NC_019843 |Middle East respiratory syndrome co...</td>\n",
              "      <td>GATTTAAGTGAATAGCTTGGCTATCTCACTTCCCCTCGTTCTCTTG...</td>\n",
              "      <td>mers-cov</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d90e05d1-337d-4963-8f97-f5622a8a6bfe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-dcb27dc4-9001-431e-97cd-8d3a444cb8d5\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dcb27dc4-9001-431e-97cd-8d3a444cb8d5')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-dcb27dc4-9001-431e-97cd-8d3a444cb8d5 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d90e05d1-337d-4963-8f97-f5622a8a6bfe 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-d90e05d1-337d-4963-8f97-f5622a8a6bfe');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Working with HCoV-OC43 Genome Sequences"
      ],
      "metadata": {
        "id": "GC3OLAH1g5zx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "convert_fasta_to_csv(datapath + 'HCoV-OC43.fasta','hcov_oc43.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w64Wj-qshVqL",
        "outputId": "4dc1cf25-4218-4176-f6c4-83c465c982cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conversion complete. CSV file saved as hcov_oc43.csv.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hCov_oc43 = pd.read_csv('hcov_oc43.csv')\n",
        "df_hCov_oc43['strain_name'] = 'hcov-OC43'\n",
        "df_hCov_oc43.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0Q6HyWwvhd5u",
        "outputId": "fb7c933a-b777-4860-9bca-e39df50e0a75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_006213.1 |Human coronavirus OC43 strain ATC...   \n",
              "1  OQ828657.1 |Human coronavirus OC43 isolate HCo...   \n",
              "2  LC756669.1 |Human coronavirus OC43 HCoVOC43_Fu...   \n",
              "3  LC756670.1 |Human coronavirus OC43 HCoVOC43_Fu...   \n",
              "4  ON461794.1 |Human coronavirus OC43 isolate OC4...   \n",
              "\n",
              "                                            Sequence strain_name  \n",
              "0  ATTGTGAGCGATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTG...   hcov-OC43  \n",
              "1  GCTTTCAGCCAGGGACGTGTTGTATCCTAGGCAGTGGCCCTCCCAT...   hcov-OC43  \n",
              "2  ATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTT...   hcov-OC43  \n",
              "3  ATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTT...   hcov-OC43  \n",
              "4  GCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTTTTGTAATC...   hcov-OC43  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-a4a5f5e4-946a-429d-bc87-74771df16c01\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_006213.1 |Human coronavirus OC43 strain ATC...</td>\n",
              "      <td>ATTGTGAGCGATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTG...</td>\n",
              "      <td>hcov-OC43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OQ828657.1 |Human coronavirus OC43 isolate HCo...</td>\n",
              "      <td>GCTTTCAGCCAGGGACGTGTTGTATCCTAGGCAGTGGCCCTCCCAT...</td>\n",
              "      <td>hcov-OC43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LC756669.1 |Human coronavirus OC43 HCoVOC43_Fu...</td>\n",
              "      <td>ATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTT...</td>\n",
              "      <td>hcov-OC43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LC756670.1 |Human coronavirus OC43 HCoVOC43_Fu...</td>\n",
              "      <td>ATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTT...</td>\n",
              "      <td>hcov-OC43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ON461794.1 |Human coronavirus OC43 isolate OC4...</td>\n",
              "      <td>GCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTTTTGTAATC...</td>\n",
              "      <td>hcov-OC43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4a5f5e4-946a-429d-bc87-74771df16c01')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-4f15ed30-7c6c-4dfb-99ca-1aa0c5be1a1d\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4f15ed30-7c6c-4dfb-99ca-1aa0c5be1a1d')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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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              "  }\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-4f15ed30-7c6c-4dfb-99ca-1aa0c5be1a1d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a4a5f5e4-946a-429d-bc87-74771df16c01 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-a4a5f5e4-946a-429d-bc87-74771df16c01');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hCov_oc43.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NP5epT9XhnzU",
        "outputId": "8ca0c9b2-9130-4435-9807-ff2f51bd2365"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_hCov_oc43 = maxSeq_length(df_hCov_oc43['Sequence'])\n",
        "max_len_hCov_oc43"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z1VBKm34hqKW",
        "outputId": "0e18c9c1-b685-40b5-eb7b-153d89e05fd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "31104"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_len_hCov_oc43 = minSeq_length(df_hCov_oc43['Sequence'])\n",
        "min_len_hCov_oc43"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oeXgRTMrhr6R",
        "outputId": "34fe7d3f-b23b-4435-9989-447c69258b7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "123"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hCov_oc43 = df_hCov_oc43[(df_hCov_oc43['Sequence'].str.len() > 25000)]\n",
        "df_hCov_oc43.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CQh1dSyZhtmy",
        "outputId": "9f9388a0-b4f0-4312-fa96-ca822a56c9b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(117, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Labeling 'hcov-OC43' as '2'\n",
        "df_hCov_oc43['label'] = 2\n",
        "df_hCov_oc43.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ympPUyhVhvpM",
        "outputId": "9085faa4-93d7-46cd-8f8b-fe7d28df1879"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_006213.1 |Human coronavirus OC43 strain ATC...   \n",
              "1  OQ828657.1 |Human coronavirus OC43 isolate HCo...   \n",
              "2  LC756669.1 |Human coronavirus OC43 HCoVOC43_Fu...   \n",
              "3  LC756670.1 |Human coronavirus OC43 HCoVOC43_Fu...   \n",
              "4  ON461794.1 |Human coronavirus OC43 isolate OC4...   \n",
              "\n",
              "                                            Sequence strain_name  label  \n",
              "0  ATTGTGAGCGATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTG...   hcov-OC43      2  \n",
              "1  GCTTTCAGCCAGGGACGTGTTGTATCCTAGGCAGTGGCCCTCCCAT...   hcov-OC43      2  \n",
              "2  ATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTT...   hcov-OC43      2  \n",
              "3  ATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTT...   hcov-OC43      2  \n",
              "4  GCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTTTTGTAATC...   hcov-OC43      2  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-09fcdcde-9f55-4bf3-ba2e-9b1006722d58\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_006213.1 |Human coronavirus OC43 strain ATC...</td>\n",
              "      <td>ATTGTGAGCGATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTG...</td>\n",
              "      <td>hcov-OC43</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OQ828657.1 |Human coronavirus OC43 isolate HCo...</td>\n",
              "      <td>GCTTTCAGCCAGGGACGTGTTGTATCCTAGGCAGTGGCCCTCCCAT...</td>\n",
              "      <td>hcov-OC43</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LC756669.1 |Human coronavirus OC43 HCoVOC43_Fu...</td>\n",
              "      <td>ATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTT...</td>\n",
              "      <td>hcov-OC43</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>LC756670.1 |Human coronavirus OC43 HCoVOC43_Fu...</td>\n",
              "      <td>ATTTGCGTGCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTT...</td>\n",
              "      <td>hcov-OC43</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ON461794.1 |Human coronavirus OC43 isolate OC4...</td>\n",
              "      <td>GCGTGCATCCCGCTTCACTGATCTCTTGTTAGATCTTTTTGTAATC...</td>\n",
              "      <td>hcov-OC43</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09fcdcde-9f55-4bf3-ba2e-9b1006722d58')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-ec5e3ea8-44b1-4f21-b76c-12519e9e9474\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ec5e3ea8-44b1-4f21-b76c-12519e9e9474')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-ec5e3ea8-44b1-4f21-b76c-12519e9e9474 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-09fcdcde-9f55-4bf3-ba2e-9b1006722d58 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-09fcdcde-9f55-4bf3-ba2e-9b1006722d58');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Working with HCoV-229E Genome Sequences\n"
      ],
      "metadata": {
        "id": "38fGazhZh2hC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "convert_fasta_to_csv(datapath + 'HCoV-229E.fasta','hcov_229E.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WJ__bmpeh-jf",
        "outputId": "5ce72831-6ce5-4593-9391-041c659358be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conversion complete. CSV file saved as hcov_229E.csv.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hCov_229e = pd.read_csv('hcov_229E.csv')\n",
        "df_hCov_229e['strain_name'] = 'hcov-229E'\n",
        "df_hCov_229e.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Co-DXFrgiA-Y",
        "outputId": "2a5687f1-011f-4a00-a1a7-c3972eaa0702"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_028752.1 |Camel alphacoronavirus isolate ca...   \n",
              "1  NC_002645.1 |Human coronavirus 229E, complete ...   \n",
              "2  OQ920097.1 |Mutant Human coronavirus 229E isol...   \n",
              "3  OQ920098.1 |Mutant Human coronavirus 229E isol...   \n",
              "4  OQ920099.1 |Mutant Human coronavirus 229E isol...   \n",
              "\n",
              "                                            Sequence strain_name  \n",
              "0  ACTTAAGTGTCTTATCTATCTATAGATAGAAAAGTCGCTTTTTAGA...   hcov-229E  \n",
              "1  ACTTAAGTACCTTATCTATCTACAGATAGAAAAGTTGCTTTTTAGA...   hcov-229E  \n",
              "2  TTATCTATCTACAGATAGAAAAGTTGCTTTTTAGACTTTGTGTCTA...   hcov-229E  \n",
              "3  TTATCTATCTACAGATAGAAAAGTTGCTTTTTAGACTTTGTGTCTA...   hcov-229E  \n",
              "4  TTATCTATCTACAGATAGAAAAGTTGCTTTTTAGACTTTGTGTCTA...   hcov-229E  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-6c3d63a9-6c20-47de-8792-de282b190f89\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_028752.1 |Camel alphacoronavirus isolate ca...</td>\n",
              "      <td>ACTTAAGTGTCTTATCTATCTATAGATAGAAAAGTCGCTTTTTAGA...</td>\n",
              "      <td>hcov-229E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NC_002645.1 |Human coronavirus 229E, complete ...</td>\n",
              "      <td>ACTTAAGTACCTTATCTATCTACAGATAGAAAAGTTGCTTTTTAGA...</td>\n",
              "      <td>hcov-229E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OQ920097.1 |Mutant Human coronavirus 229E isol...</td>\n",
              "      <td>TTATCTATCTACAGATAGAAAAGTTGCTTTTTAGACTTTGTGTCTA...</td>\n",
              "      <td>hcov-229E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OQ920098.1 |Mutant Human coronavirus 229E isol...</td>\n",
              "      <td>TTATCTATCTACAGATAGAAAAGTTGCTTTTTAGACTTTGTGTCTA...</td>\n",
              "      <td>hcov-229E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OQ920099.1 |Mutant Human coronavirus 229E isol...</td>\n",
              "      <td>TTATCTATCTACAGATAGAAAAGTTGCTTTTTAGACTTTGTGTCTA...</td>\n",
              "      <td>hcov-229E</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c3d63a9-6c20-47de-8792-de282b190f89')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-d2c68df6-f30a-4f67-af5e-dd27eb1f88b2\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d2c68df6-f30a-4f67-af5e-dd27eb1f88b2')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-d2c68df6-f30a-4f67-af5e-dd27eb1f88b2 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6c3d63a9-6c20-47de-8792-de282b190f89 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-6c3d63a9-6c20-47de-8792-de282b190f89');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hCov_229e.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eiVwecqhiG8d",
        "outputId": "6e353362-9924-4147-d84a-a82e74f6bd46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_hCov_229e = maxSeq_length(df_hCov_229e['Sequence'])\n",
        "max_len_hCov_229e"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KM7xFyXJiKSU",
        "outputId": "85e7ece8-12b3-43b9-8840-477ed1d792a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27395"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_len_hCov_229e = minSeq_length(df_hCov_229e['Sequence'])\n",
        "min_len_hCov_229e"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kF_rbQcFiSbn",
        "outputId": "a4038996-5f34-4400-f8cb-a636f20c2e99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "286"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hCov_229e = df_hCov_229e[(df_hCov_229e['Sequence'].str.len() > 25000)] # 29903\n",
        "df_hCov_229e.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7CtZHJsiNJF",
        "outputId": "2121e72b-4614-45f2-fcd8-48d8d9c91806"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(80, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Labeling 'hCov_229e' as '3'\n",
        "df_hCov_229e['label'] = 3\n",
        "df_hCov_229e.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Vre9NOuCiOwt",
        "outputId": "4817000e-86a2-428b-af52-db0dcb656830"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_028752.1 |Camel alphacoronavirus isolate ca...   \n",
              "1  NC_002645.1 |Human coronavirus 229E, complete ...   \n",
              "3  OQ920098.1 |Mutant Human coronavirus 229E isol...   \n",
              "4  OQ920099.1 |Mutant Human coronavirus 229E isol...   \n",
              "7  ON461751.1 |Human coronavirus 229E isolate 229...   \n",
              "\n",
              "                                            Sequence strain_name  label  \n",
              "0  ACTTAAGTGTCTTATCTATCTATAGATAGAAAAGTCGCTTTTTAGA...   hcov-229E      3  \n",
              "1  ACTTAAGTACCTTATCTATCTACAGATAGAAAAGTTGCTTTTTAGA...   hcov-229E      3  \n",
              "3  TTATCTATCTACAGATAGAAAAGTTGCTTTTTAGACTTTGTGTCTA...   hcov-229E      3  \n",
              "4  TTATCTATCTACAGATAGAAAAGTTGCTTTTTAGACTTTGTGTCTA...   hcov-229E      3  \n",
              "7  GATAGAAAAGTTGCTTTTTAGACTTTGTGTCTACTTTTCTCAACTA...   hcov-229E      3  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-87ff8d99-e48e-46be-b3fc-bf9088d6ddf2\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_028752.1 |Camel alphacoronavirus isolate ca...</td>\n",
              "      <td>ACTTAAGTGTCTTATCTATCTATAGATAGAAAAGTCGCTTTTTAGA...</td>\n",
              "      <td>hcov-229E</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NC_002645.1 |Human coronavirus 229E, complete ...</td>\n",
              "      <td>ACTTAAGTACCTTATCTATCTACAGATAGAAAAGTTGCTTTTTAGA...</td>\n",
              "      <td>hcov-229E</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OQ920098.1 |Mutant Human coronavirus 229E isol...</td>\n",
              "      <td>TTATCTATCTACAGATAGAAAAGTTGCTTTTTAGACTTTGTGTCTA...</td>\n",
              "      <td>hcov-229E</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OQ920099.1 |Mutant Human coronavirus 229E isol...</td>\n",
              "      <td>TTATCTATCTACAGATAGAAAAGTTGCTTTTTAGACTTTGTGTCTA...</td>\n",
              "      <td>hcov-229E</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>ON461751.1 |Human coronavirus 229E isolate 229...</td>\n",
              "      <td>GATAGAAAAGTTGCTTTTTAGACTTTGTGTCTACTTTTCTCAACTA...</td>\n",
              "      <td>hcov-229E</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-87ff8d99-e48e-46be-b3fc-bf9088d6ddf2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-28c05fb2-d07b-403e-b9d3-7b6a710a5a76\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-28c05fb2-d07b-403e-b9d3-7b6a710a5a76')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-28c05fb2-d07b-403e-b9d3-7b6a710a5a76 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-87ff8d99-e48e-46be-b3fc-bf9088d6ddf2 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-87ff8d99-e48e-46be-b3fc-bf9088d6ddf2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Working with 'HCoV-NL63' Genome Sequences"
      ],
      "metadata": {
        "id": "gG8wq-ZfibyW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "convert_fasta_to_csv(datapath + 'HCoV-NL63.fasta','hcov_nl63.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JpFdQ2M9jcgD",
        "outputId": "04b666d5-c75c-4402-c30f-010b54a0107a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conversion complete. CSV file saved as hcov_nl63.csv.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hCov_nl63 = pd.read_csv('hcov_nl63.csv')\n",
        "df_hCov_nl63['strain_name'] = 'hcov-NL63'\n",
        "df_hCov_nl63.head()"
      ],
      "metadata": {
        "id": "TZsuqqWWjePS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b9443189-0c6a-4b85-f06a-c74984366cc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_005831.2 |Human Coronavirus NL63, complete ...   \n",
              "1  LC756668.1 |Human coronavirus NL63 HCoVNL63_Fu...   \n",
              "2  ON461782.1 |Human coronavirus NL63 isolate NL6...   \n",
              "3  ON461783.1 |Human coronavirus NL63 isolate NL6...   \n",
              "4  ON461784.1 |Human coronavirus NL63 isolate NL6...   \n",
              "\n",
              "                                            Sequence strain_name  \n",
              "0  CTTAAAGAATTTTTCTATCTATAGATAGAGAATTTTCTTATTTAGA...   hcov-NL63  \n",
              "1  ATTTTTCTATCTATAGATAGAGAATTTTCTTATTTAGACTTTGTGT...   hcov-NL63  \n",
              "2  GTTCCGTCACNGCTTATTGTGGAAGCAACGTTCTGTCGNNNNNNNN...   hcov-NL63  \n",
              "3  GTGTTGTATNNNNNNNNNNNNNNCACTGGTGATTCTGTTCACTAGT...   hcov-NL63  \n",
              "4  AAGTGGTGTTCCGTCACTGCTTATTGTGGAAGCAACGTTCTGTCGT...   hcov-NL63  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-b3d05717-296c-4dc2-b615-1baccc463120\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_005831.2 |Human Coronavirus NL63, complete ...</td>\n",
              "      <td>CTTAAAGAATTTTTCTATCTATAGATAGAGAATTTTCTTATTTAGA...</td>\n",
              "      <td>hcov-NL63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LC756668.1 |Human coronavirus NL63 HCoVNL63_Fu...</td>\n",
              "      <td>ATTTTTCTATCTATAGATAGAGAATTTTCTTATTTAGACTTTGTGT...</td>\n",
              "      <td>hcov-NL63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ON461782.1 |Human coronavirus NL63 isolate NL6...</td>\n",
              "      <td>GTTCCGTCACNGCTTATTGTGGAAGCAACGTTCTGTCGNNNNNNNN...</td>\n",
              "      <td>hcov-NL63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ON461783.1 |Human coronavirus NL63 isolate NL6...</td>\n",
              "      <td>GTGTTGTATNNNNNNNNNNNNNNCACTGGTGATTCTGTTCACTAGT...</td>\n",
              "      <td>hcov-NL63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ON461784.1 |Human coronavirus NL63 isolate NL6...</td>\n",
              "      <td>AAGTGGTGTTCCGTCACTGCTTATTGTGGAAGCAACGTTCTGTCGT...</td>\n",
              "      <td>hcov-NL63</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert: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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b3d05717-296c-4dc2-b615-1baccc463120 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-b3d05717-296c-4dc2-b615-1baccc463120');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_hCov_nl63 = max([len(seq) for seq in df_hCov_nl63['Sequence']])\n",
        "max_len_hCov_nl63"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REqcIA1djl-R",
        "outputId": "d94de7b7-eaf8-44ed-90f0-77d3d74a8a49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27590"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_len_hCov_nl63 = min([len(seq) for seq in df_hCov_nl63['Sequence']])\n",
        "min_len_hCov_nl63"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PbubJjzPjn8C",
        "outputId": "3004fcd8-4fa4-4cd9-cd9b-ef592d1efd63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "286"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hCov_nl63 = df_hCov_nl63[(df_hCov_nl63['Sequence'].str.len() > 25000)]\n",
        "df_hCov_nl63.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__P7anVdjpwg",
        "outputId": "a8f8371f-a315-4c97-d8c9-a2874ad00746"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(102, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Labeling 'hCov_nl63' as '4'\n",
        "df_hCov_nl63['label'] = 4\n",
        "df_hCov_nl63.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "p59rBgFvjsqf",
        "outputId": "0b541cd5-9f5b-46d7-9da3-4079f9f8c901"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_005831.2 |Human Coronavirus NL63, complete ...   \n",
              "1  LC756668.1 |Human coronavirus NL63 HCoVNL63_Fu...   \n",
              "2  ON461782.1 |Human coronavirus NL63 isolate NL6...   \n",
              "3  ON461783.1 |Human coronavirus NL63 isolate NL6...   \n",
              "4  ON461784.1 |Human coronavirus NL63 isolate NL6...   \n",
              "\n",
              "                                            Sequence strain_name  label  \n",
              "0  CTTAAAGAATTTTTCTATCTATAGATAGAGAATTTTCTTATTTAGA...   hcov-NL63      4  \n",
              "1  ATTTTTCTATCTATAGATAGAGAATTTTCTTATTTAGACTTTGTGT...   hcov-NL63      4  \n",
              "2  GTTCCGTCACNGCTTATTGTGGAAGCAACGTTCTGTCGNNNNNNNN...   hcov-NL63      4  \n",
              "3  GTGTTGTATNNNNNNNNNNNNNNCACTGGTGATTCTGTTCACTAGT...   hcov-NL63      4  \n",
              "4  AAGTGGTGTTCCGTCACTGCTTATTGTGGAAGCAACGTTCTGTCGT...   hcov-NL63      4  "
            ],
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              "      <th></th>\n",
              "      <th>Sequence ID</th>\n",
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              "      <th>0</th>\n",
              "      <td>NC_005831.2 |Human Coronavirus NL63, complete ...</td>\n",
              "      <td>CTTAAAGAATTTTTCTATCTATAGATAGAGAATTTTCTTATTTAGA...</td>\n",
              "      <td>hcov-NL63</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LC756668.1 |Human coronavirus NL63 HCoVNL63_Fu...</td>\n",
              "      <td>ATTTTTCTATCTATAGATAGAGAATTTTCTTATTTAGACTTTGTGT...</td>\n",
              "      <td>hcov-NL63</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ON461782.1 |Human coronavirus NL63 isolate NL6...</td>\n",
              "      <td>GTTCCGTCACNGCTTATTGTGGAAGCAACGTTCTGTCGNNNNNNNN...</td>\n",
              "      <td>hcov-NL63</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ON461783.1 |Human coronavirus NL63 isolate NL6...</td>\n",
              "      <td>GTGTTGTATNNNNNNNNNNNNNNCACTGGTGATTCTGTTCACTAGT...</td>\n",
              "      <td>hcov-NL63</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ON461784.1 |Human coronavirus NL63 isolate NL6...</td>\n",
              "      <td>AAGTGGTGTTCCGTCACTGCTTATTGTGGAAGCAACGTTCTGTCGT...</td>\n",
              "      <td>hcov-NL63</td>\n",
              "      <td>4</td>\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-quickchart:hover {\n",
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              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-db4870a4-8c67-4172-a225-9033a93a7ed7 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert: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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4065577a-c9e1-4831-9cb7-b6879b3638c0 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-4065577a-c9e1-4831-9cb7-b6879b3638c0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Working with HCoV-HKU1 Genome Sequences"
      ],
      "metadata": {
        "id": "6wbRqfjPjv1a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "convert_fasta_to_csv(datapath + 'HCoV-HKU1.fasta','hcov_hku1.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qGm_RFSgj7Em",
        "outputId": "8ad8a54f-cfe6-40b0-90be-f390449cbe19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conversion complete. CSV file saved as hcov_hku1.csv.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hCov_hku1= pd.read_csv('hcov_hku1.csv')\n",
        "df_hCov_hku1['strain_name'] = 'hcov-HKU1'\n",
        "df_hCov_hku1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OMaWb1GFj8qw",
        "outputId": "d1566ac2-9af5-429a-a56f-7f887baaf287"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_006577.2 |Human coronavirus HKU1, complete ...   \n",
              "1  ON381261.1 |Human coronavirus HKU1 isolate KHA...   \n",
              "2  ON461755.1 |Human coronavirus HKU1 isolate HKU...   \n",
              "3  ON461756.1 |Human coronavirus HKU1 isolate HKU...   \n",
              "4  ON461757.1 |Human coronavirus HKU1 isolate HKU...   \n",
              "\n",
              "                                            Sequence strain_name  \n",
              "0  GAGTTTGAGCGATTGACGTTCGTACCGTCTATCAGCTTACGATCTC...   hcov-HKU1  \n",
              "1  AGAGCTATGCCAAATATTTTGCGTATTGTTAGTAGTTTAGTTTTGG...   hcov-HKU1  \n",
              "2  TCTATCAGCTTACGATCTCTTGTCAGATCTCATTAAATCTAAACTT...   hcov-HKU1  \n",
              "3  ATTGACGTTCGTACCGTCTATCAGCTTACGATCTCTTGTCAGATCT...   hcov-HKU1  \n",
              "4  TTGAGCGATTGACGTTCGTACCGTCTATCAGCTTACGATCTCTTGT...   hcov-HKU1  "
            ],
            "text/html": [
              "\n",
              "\n",
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              "      <th></th>\n",
              "      <th>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_006577.2 |Human coronavirus HKU1, complete ...</td>\n",
              "      <td>GAGTTTGAGCGATTGACGTTCGTACCGTCTATCAGCTTACGATCTC...</td>\n",
              "      <td>hcov-HKU1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ON381261.1 |Human coronavirus HKU1 isolate KHA...</td>\n",
              "      <td>AGAGCTATGCCAAATATTTTGCGTATTGTTAGTAGTTTAGTTTTGG...</td>\n",
              "      <td>hcov-HKU1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ON461755.1 |Human coronavirus HKU1 isolate HKU...</td>\n",
              "      <td>TCTATCAGCTTACGATCTCTTGTCAGATCTCATTAAATCTAAACTT...</td>\n",
              "      <td>hcov-HKU1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ON461756.1 |Human coronavirus HKU1 isolate HKU...</td>\n",
              "      <td>ATTGACGTTCGTACCGTCTATCAGCTTACGATCTCTTGTCAGATCT...</td>\n",
              "      <td>hcov-HKU1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ON461757.1 |Human coronavirus HKU1 isolate HKU...</td>\n",
              "      <td>TTGAGCGATTGACGTTCGTACCGTCTATCAGCTTACGATCTCTTGT...</td>\n",
              "      <td>hcov-HKU1</td>\n",
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              "      fill: #174EA6;\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-ebc0e86f-7c5a-4f96-a5e3-4613f59f0a77 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-ebc0e86f-7c5a-4f96-a5e3-4613f59f0a77');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_hCov_hku1 = max([len(seq) for seq in df_hCov_hku1['Sequence']])\n",
        "max_len_hCov_hku1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOY3m5F0kAQK",
        "outputId": "6215bb8c-5e51-49e1-e5d7-cd05e23022db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "30144"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_len_hCov_hku1 = min([len(seq) for seq in df_hCov_hku1['Sequence']])\n",
        "min_len_hCov_hku1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jgqQenHDkB-2",
        "outputId": "fa06526b-1428-432f-f961-80018e1e081d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "212"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_hCov_hku1 = df_hCov_hku1[(df_hCov_hku1['Sequence'].str.len() > 25000)]\n",
        "df_hCov_hku1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6GUoJr1KkHma",
        "outputId": "d6c85e07-5b28-41c2-b1fb-498e891dce2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Labeling 'hCov_hku1' as '5'\n",
        "df_hCov_hku1['label'] = 5\n",
        "df_hCov_hku1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CCw4hWiykJHp",
        "outputId": "d345cb2d-36f5-40c0-ac69-4a76e0c8e3ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_006577.2 |Human coronavirus HKU1, complete ...   \n",
              "1  ON381261.1 |Human coronavirus HKU1 isolate KHA...   \n",
              "2  ON461755.1 |Human coronavirus HKU1 isolate HKU...   \n",
              "3  ON461756.1 |Human coronavirus HKU1 isolate HKU...   \n",
              "4  ON461757.1 |Human coronavirus HKU1 isolate HKU...   \n",
              "\n",
              "                                            Sequence strain_name  label  \n",
              "0  GAGTTTGAGCGATTGACGTTCGTACCGTCTATCAGCTTACGATCTC...   hcov-HKU1      5  \n",
              "1  AGAGCTATGCCAAATATTTTGCGTATTGTTAGTAGTTTAGTTTTGG...   hcov-HKU1      5  \n",
              "2  TCTATCAGCTTACGATCTCTTGTCAGATCTCATTAAATCTAAACTT...   hcov-HKU1      5  \n",
              "3  ATTGACGTTCGTACCGTCTATCAGCTTACGATCTCTTGTCAGATCT...   hcov-HKU1      5  \n",
              "4  TTGAGCGATTGACGTTCGTACCGTCTATCAGCTTACGATCTCTTGT...   hcov-HKU1      5  "
            ],
            "text/html": [
              "\n",
              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th>0</th>\n",
              "      <td>NC_006577.2 |Human coronavirus HKU1, complete ...</td>\n",
              "      <td>GAGTTTGAGCGATTGACGTTCGTACCGTCTATCAGCTTACGATCTC...</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ON381261.1 |Human coronavirus HKU1 isolate KHA...</td>\n",
              "      <td>AGAGCTATGCCAAATATTTTGCGTATTGTTAGTAGTTTAGTTTTGG...</td>\n",
              "      <td>hcov-HKU1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ON461755.1 |Human coronavirus HKU1 isolate HKU...</td>\n",
              "      <td>TCTATCAGCTTACGATCTCTTGTCAGATCTCATTAAATCTAAACTT...</td>\n",
              "      <td>hcov-HKU1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ON461756.1 |Human coronavirus HKU1 isolate HKU...</td>\n",
              "      <td>ATTGACGTTCGTACCGTCTATCAGCTTACGATCTCTTGTCAGATCT...</td>\n",
              "      <td>hcov-HKU1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ON461757.1 |Human coronavirus HKU1 isolate HKU...</td>\n",
              "      <td>TTGAGCGATTGACGTTCGTACCGTCTATCAGCTTACGATCTCTTGT...</td>\n",
              "      <td>hcov-HKU1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9e2f9319-c3f7-484f-ad92-8914745043a3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8100f9e9-3874-4161-998e-54de3f48c866')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
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              "<style>\n",
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              "    width: 32px;\n",
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              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
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              "      <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\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-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9e2f9319-c3f7-484f-ad92-8914745043a3 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-9e2f9319-c3f7-484f-ad92-8914745043a3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Working with SARS-CoV Genome Sequences"
      ],
      "metadata": {
        "id": "PfBUZReXkNeh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "convert_fasta_to_csv(datapath + 'SARSr-COV.fasta', 'sars-cov.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w1iC3OGykT8k",
        "outputId": "3483b6a7-76ec-4a81-ea82-7e5f202e6c69"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Conversion complete. CSV file saved as sars-cov.csv.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_sarsCov = pd.read_csv('sars-cov.csv')\n",
        "df_sarsCov['strain_name'] = 'sars-cov'\n",
        "df_sarsCov.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "voCGVpfHkVX0",
        "outputId": "53eaa5ae-574e-4d40-99ad-7f518b4530f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_045512 |Wuhan seafood market pneumonia viru...   \n",
              "1       NC_004718 |SARS coronavirus| complete genome   \n",
              "2  MT186676 |Severe acute respiratory syndrome co...   \n",
              "3  MT186677 |Severe acute respiratory syndrome co...   \n",
              "4  MT186678 |Severe acute respiratory syndrome co...   \n",
              "\n",
              "                                            Sequence strain_name  \n",
              "0  ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...    sars-cov  \n",
              "1  ATATTAGGTTTTTACCTACCCAGGAAAAGCCAACCAACCTCGATCT...    sars-cov  \n",
              "2  CGCGATCAAAACAACGTCGGCCCCAAGGTTTACCCAATAATACTGC...    sars-cov  \n",
              "3  CGCGATCAAAACAACGTCGGCCCCAAGGTTTACCCAATAATACTGC...    sars-cov  \n",
              "4  CGCGATCAAAACAACGTCGGCCCCAAGGTTTACCCAATAATACTGC...    sars-cov  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-529e9de9-ff53-4f69-aa9b-ef59950c96f8\">\n",
              "    <div 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>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_045512 |Wuhan seafood market pneumonia viru...</td>\n",
              "      <td>ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...</td>\n",
              "      <td>sars-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NC_004718 |SARS coronavirus| complete genome</td>\n",
              "      <td>ATATTAGGTTTTTACCTACCCAGGAAAAGCCAACCAACCTCGATCT...</td>\n",
              "      <td>sars-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MT186676 |Severe acute respiratory syndrome co...</td>\n",
              "      <td>CGCGATCAAAACAACGTCGGCCCCAAGGTTTACCCAATAATACTGC...</td>\n",
              "      <td>sars-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MT186677 |Severe acute respiratory syndrome co...</td>\n",
              "      <td>CGCGATCAAAACAACGTCGGCCCCAAGGTTTACCCAATAATACTGC...</td>\n",
              "      <td>sars-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MT186678 |Severe acute respiratory syndrome co...</td>\n",
              "      <td>CGCGATCAAAACAACGTCGGCCCCAAGGTTTACCCAATAATACTGC...</td>\n",
              "      <td>sars-cov</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-529e9de9-ff53-4f69-aa9b-ef59950c96f8')\"\n",
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              "              style=\"display:none;\">\n",
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              "    </g>\n",
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              "      </button>\n",
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              "  .colab-df-quickchart {\n",
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              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart: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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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
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              "\n",
              "      <script>\n",
              "\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      display: none;\n",
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              "      width: 32px;\n",
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              "\n",
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              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-529e9de9-ff53-4f69-aa9b-ef59950c96f8 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-529e9de9-ff53-4f69-aa9b-ef59950c96f8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_sarsCov['Sequence'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyws6ehYkhUA",
        "outputId": "36d7ea71-c9f8-4dda-cd46-26f970d0ca44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "957"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing duplicate rows based on 'Sequence' column\n",
        "df_sarsCov = df_sarsCov.drop_duplicates(subset='Sequence')\n",
        "df_sarsCov.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oeW_9hH_klfB",
        "outputId": "9c467d83-ab06-4862-ee9a-8c4eb2a64ef0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(957, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_sarsCov = maxSeq_length(df_sarsCov['Sequence'])\n",
        "max_len_sarsCov"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jy9yYzliknOR",
        "outputId": "3a7e3b5a-83ce-4f6e-f1ea-67b1ec0e9767"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37971"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_len_sarsCov = minSeq_length(df_sarsCov['Sequence'])\n",
        "min_len_sarsCov"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZwS66CE2kph7",
        "outputId": "de383dcb-8de5-4619-8c67-d618792799e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_sarsCov = df_sarsCov[df_sarsCov['Sequence'].str.len() > 25000]\n",
        "df_sarsCov.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHBdvJhskrG2",
        "outputId": "f0397c71-82b9-44b4-cc8f-0864c22482df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(348, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_sarsCov.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Pgm-UCr_ksin",
        "outputId": "e2951980-e11c-4044-9a94-fa217c6e37d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          Sequence ID  \\\n",
              "0   NC_045512 |Wuhan seafood market pneumonia viru...   \n",
              "1        NC_004718 |SARS coronavirus| complete genome   \n",
              "9   MT188339 |Severe acute respiratory syndrome co...   \n",
              "10  MT188340 |Severe acute respiratory syndrome co...   \n",
              "11  MT188341 |Severe acute respiratory syndrome co...   \n",
              "\n",
              "                                             Sequence strain_name  \n",
              "0   ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...    sars-cov  \n",
              "1   ATATTAGGTTTTTACCTACCCAGGAAAAGCCAACCAACCTCGATCT...    sars-cov  \n",
              "9   AGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...    sars-cov  \n",
              "10  AGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...    sars-cov  \n",
              "11  AGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...    sars-cov  "
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              "      <td>NC_045512 |Wuhan seafood market pneumonia viru...</td>\n",
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              "      <th>1</th>\n",
              "      <td>NC_004718 |SARS coronavirus| complete genome</td>\n",
              "      <td>ATATTAGGTTTTTACCTACCCAGGAAAAGCCAACCAACCTCGATCT...</td>\n",
              "      <td>sars-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>MT188339 |Severe acute respiratory syndrome co...</td>\n",
              "      <td>AGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...</td>\n",
              "      <td>sars-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>MT188340 |Severe acute respiratory syndrome co...</td>\n",
              "      <td>AGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...</td>\n",
              "      <td>sars-cov</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>MT188341 |Severe acute respiratory syndrome co...</td>\n",
              "      <td>AGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...</td>\n",
              "      <td>sars-cov</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-584c5fa3-6836-4aa3-b212-53ca36f6f15d')\"\n",
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              "              title=\"Suggest charts.\"\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart: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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              "\n",
              "    <script>\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert: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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-584c5fa3-6836-4aa3-b212-53ca36f6f15d 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-584c5fa3-6836-4aa3-b212-53ca36f6f15d');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_len_sarsCov = maxSeq_length(df_sarsCov['Sequence'])\n",
        "max_len_sarsCov"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fydsakGQkuC9",
        "outputId": "630feda9-73cf-49e0-8cb0-db630eb95ae3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "37971"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Labeling 'sars-Cov' as '6'\n",
        "df_sarsCov['label'] = 6\n",
        "df_sarsCov.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "01Jugm8tkzsg",
        "outputId": "1726261d-7026-4b3b-8c50-4e99c119e10c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          Sequence ID  \\\n",
              "0   NC_045512 |Wuhan seafood market pneumonia viru...   \n",
              "1        NC_004718 |SARS coronavirus| complete genome   \n",
              "9   MT188339 |Severe acute respiratory syndrome co...   \n",
              "10  MT188340 |Severe acute respiratory syndrome co...   \n",
              "11  MT188341 |Severe acute respiratory syndrome co...   \n",
              "\n",
              "                                             Sequence strain_name  label  \n",
              "0   ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...    sars-cov      6  \n",
              "1   ATATTAGGTTTTTACCTACCCAGGAAAAGCCAACCAACCTCGATCT...    sars-cov      6  \n",
              "9   AGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...    sars-cov      6  \n",
              "10  AGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...    sars-cov      6  \n",
              "11  AGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...    sars-cov      6  "
            ],
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              "      <th>1</th>\n",
              "      <td>NC_004718 |SARS coronavirus| complete genome</td>\n",
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              "      <th>9</th>\n",
              "      <td>MT188339 |Severe acute respiratory syndrome co...</td>\n",
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              "      <th>11</th>\n",
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              "      <td>AGATCTGTTCTCTAAACGAACTTTAAAATCTGTGTGGCTGTCACTC...</td>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ca39f434-a932-4416-8a63-785fc79cc4bd 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-ca39f434-a932-4416-8a63-785fc79cc4bd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Maximum length among all genome sequences"
      ],
      "metadata": {
        "id": "zfWOv3Xuk2zm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"max_len_sarsCov2 = {max_len_sarsCov2}\")\n",
        "print(f\"max_len_mersCov = {max_len_mersCov}\")\n",
        "print(f\"max_len_hCov_oc43 = {max_len_hCov_oc43}\")\n",
        "print(f\"max_len_hCov_229e = {max_len_hCov_229e}\")\n",
        "print(f\"max_len_hCov_nl63 = {max_len_hCov_nl63}\")\n",
        "print(f\"max_len_hCov_hku1 = {max_len_hCov_hku1}\")\n",
        "print(f\"max_len_sarsCov = {max_len_sarsCov}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoMUS3jNk8-v",
        "outputId": "3b23aa9c-c5ff-4890-a99d-4b092261758c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "max_len_sarsCov2 = 29903\n",
            "max_len_mersCov = 30484\n",
            "max_len_hCov_oc43 = 31104\n",
            "max_len_hCov_229e = 27395\n",
            "max_len_hCov_nl63 = 27590\n",
            "max_len_hCov_hku1 = 30144\n",
            "max_len_sarsCov = 37971\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merging all the dataframe together and making it a single dataframe"
      ],
      "metadata": {
        "id": "omXEPL_ck923"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.concat([df_sarsCov2, df_mersCov, df_hCov_oc43, df_hCov_229e, df_hCov_nl63, df_hCov_hku1, df_sarsCov],\n",
        "                 axis=0)"
      ],
      "metadata": {
        "id": "VICRauQQlMjW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WSEHmCVlNrf",
        "outputId": "8c852a33-816a-4012-8a03-c33288c78991"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Sequence ID    1781\n",
              "Sequence       1747\n",
              "strain_name       7\n",
              "label             7\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XaBIxRGTkfEd",
        "outputId": "c4c568fb-5b9a-463c-a003-241e702d8084"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1781, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Mapping the labels to their corresponding names\n",
        "label_mapping = {\n",
        "    0: 'SARS-CoV2',\n",
        "    1: 'MERS-CoV',\n",
        "    2: 'HCoV-OC43',\n",
        "    3: 'HCoV-229E',\n",
        "    4: 'HCoV-NL63',\n",
        "    5: 'HCoV-HKU1',\n",
        "    6: 'SARS-CoV'\n",
        "}\n",
        "\n",
        "# Replacing label values with their corresponding names\n",
        "data['label_names'] = data['label'].map(label_mapping)\n",
        "\n",
        "# Creating the countplot with custom labels\n",
        "sns.countplot(x=data['label_names'])\n",
        "\n",
        "# Rotating x-axis labels for better readability\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "dgUOnUbcj1Cp",
        "outputId": "5fb0ae53-7d96-4ab8-cc75-9ddf3ec8640c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Modifying the data by removing some columns"
      ],
      "metadata": {
        "id": "UudChx5pl_Ne"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sMd5VwzymE-r",
        "outputId": "528bd698-e2b0-4648-81f8-30ed9d6c79b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Sequence ID  \\\n",
              "0  NC_045512.2 |Severe acute respiratory syndrome...   \n",
              "1  OR177857.1 |Severe acute respiratory syndrome ...   \n",
              "2  OR177858.1 |Severe acute respiratory syndrome ...   \n",
              "3  OR177859.1 |Severe acute respiratory syndrome ...   \n",
              "4  OR177860.1 |Severe acute respiratory syndrome ...   \n",
              "\n",
              "                                            Sequence strain_name  label  \\\n",
              "0  ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...   sars-cov2      0   \n",
              "1  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "2  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "3  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "4  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "\n",
              "  label_names  \n",
              "0   SARS-CoV2  \n",
              "1   SARS-CoV2  \n",
              "2   SARS-CoV2  \n",
              "3   SARS-CoV2  \n",
              "4   SARS-CoV2  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-27d40d87-bc77-4e3f-9e54-30ee7b7016a7\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sequence ID</th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "      <th>label</th>\n",
              "      <th>label_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>NC_045512.2 |Severe acute respiratory syndrome...</td>\n",
              "      <td>ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OR177857.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OR177858.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OR177859.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>OR177860.1 |Severe acute respiratory syndrome ...</td>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-27d40d87-bc77-4e3f-9e54-30ee7b7016a7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-87fa2565-e8dd-4320-bf4a-a40855547718\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-87fa2565-e8dd-4320-bf4a-a40855547718')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-87fa2565-e8dd-4320-bf4a-a40855547718 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-27d40d87-bc77-4e3f-9e54-30ee7b7016a7 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-27d40d87-bc77-4e3f-9e54-30ee7b7016a7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop('Sequence ID', axis=1, inplace=True)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "5xohw-NomOU0",
        "outputId": "495646a7-5feb-4f57-90be-0fe356962331"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Sequence strain_name  label  \\\n",
              "0     ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...   sars-cov2      0   \n",
              "1     TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "2     TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "3     TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "4     TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "...                                                 ...         ...    ...   \n",
              "1565  CTACCCAGGAAAAGCCAACCAACCTCGATCTCTTGTAGATCTGTTC...    sars-cov      6   \n",
              "1568  TACCCAGGAAAAGCCAACCAACCTCGATCTCTTGTAGATCTGTTCT...    sars-cov      6   \n",
              "1570  ATATTAGGTTTTTACCTACCCAGGAAAAGCCAACCAACCTCGATCT...    sars-cov      6   \n",
              "1571  TAGGTTTTTACCTACCCAGGAAAAGCCAACCAACCTCGATCTCTTG...    sars-cov      6   \n",
              "1572  CCAGGAAAAGCCAACCAACCTCGATCTCTTGTAGATCTGTTCTCTA...    sars-cov      6   \n",
              "\n",
              "     label_names  \n",
              "0      SARS-CoV2  \n",
              "1      SARS-CoV2  \n",
              "2      SARS-CoV2  \n",
              "3      SARS-CoV2  \n",
              "4      SARS-CoV2  \n",
              "...          ...  \n",
              "1565    SARS-CoV  \n",
              "1568    SARS-CoV  \n",
              "1570    SARS-CoV  \n",
              "1571    SARS-CoV  \n",
              "1572    SARS-CoV  \n",
              "\n",
              "[1781 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-1748c46a-c7c4-4f27-a9d8-5ea95f6accb5\">\n",
              "    <div 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",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "      <th>label</th>\n",
              "      <th>label_names</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1565</th>\n",
              "      <td>CTACCCAGGAAAAGCCAACCAACCTCGATCTCTTGTAGATCTGTTC...</td>\n",
              "      <td>sars-cov</td>\n",
              "      <td>6</td>\n",
              "      <td>SARS-CoV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1568</th>\n",
              "      <td>TACCCAGGAAAAGCCAACCAACCTCGATCTCTTGTAGATCTGTTCT...</td>\n",
              "      <td>sars-cov</td>\n",
              "      <td>6</td>\n",
              "      <td>SARS-CoV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1570</th>\n",
              "      <td>ATATTAGGTTTTTACCTACCCAGGAAAAGCCAACCAACCTCGATCT...</td>\n",
              "      <td>sars-cov</td>\n",
              "      <td>6</td>\n",
              "      <td>SARS-CoV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1571</th>\n",
              "      <td>TAGGTTTTTACCTACCCAGGAAAAGCCAACCAACCTCGATCTCTTG...</td>\n",
              "      <td>sars-cov</td>\n",
              "      <td>6</td>\n",
              "      <td>SARS-CoV</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1572</th>\n",
              "      <td>CCAGGAAAAGCCAACCAACCTCGATCTCTTGTAGATCTGTTCTCTA...</td>\n",
              "      <td>sars-cov</td>\n",
              "      <td>6</td>\n",
              "      <td>SARS-CoV</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1781 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1748c46a-c7c4-4f27-a9d8-5ea95f6accb5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-7c404f1e-ec91-4ebd-944e-e3821c71d01e\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7c404f1e-ec91-4ebd-944e-e3821c71d01e')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-7c404f1e-ec91-4ebd-944e-e3821c71d01e button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1748c46a-c7c4-4f27-a9d8-5ea95f6accb5 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-1748c46a-c7c4-4f27-a9d8-5ea95f6accb5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### shape of the dataframe\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6va-gA23mh8f",
        "outputId": "91974d3f-4fee-465c-857c-323f519f344d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1781, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## function for transforming genome sequence\n",
        "nucleotide_values = {\n",
        "    'A': 1,\n",
        "    'T': 2,\n",
        "    'C': 3,\n",
        "    'G': 4\n",
        "}\n",
        "\n",
        "def transform_sequence(sequence):\n",
        "    result = []\n",
        "    for nucleotide in sequence:\n",
        "        value = nucleotide_values.get(nucleotide, 0)\n",
        "        result.append(value)\n",
        "    return result\n"
      ],
      "metadata": {
        "id": "tKMLeZt9mjt0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['transformed_sequence'] = data['Sequence'].apply(transform_sequence)"
      ],
      "metadata": {
        "id": "It2m8YD-mp1N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "iXZtvAdimrwN",
        "outputId": "8f0cb923-ffa4-4466-9b76-9ea86af1ceef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            Sequence strain_name  label  \\\n",
              "0  ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...   sars-cov2      0   \n",
              "1  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "2  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "3  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "4  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "\n",
              "  label_names                               transformed_sequence  \n",
              "0   SARS-CoV2  [1, 2, 2, 1, 1, 1, 4, 4, 2, 2, 2, 1, 2, 1, 3, ...  \n",
              "1   SARS-CoV2  [2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...  \n",
              "2   SARS-CoV2  [2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...  \n",
              "3   SARS-CoV2  [2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...  \n",
              "4   SARS-CoV2  [2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...  "
            ],
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
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              "      <th>label_names</th>\n",
              "      <th>transformed_sequence</th>\n",
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              "      <th>0</th>\n",
              "      <td>ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "      <td>[1, 2, 2, 1, 1, 1, 4, 4, 2, 2, 2, 1, 2, 1, 3, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "      <td>[2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "      <td>[2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "      <td>[2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "      <td>[2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "    </g>\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: #1967D2;\n",
              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
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              "\n",
              "      <script>\n",
              "\n",
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              "    domScope.querySelector('#df-75aac501-d82c-4656-a1c6-7e931b797422 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert: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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-12632a71-bbeb-4a84-a1d1-bb4a791df15d 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-12632a71-bbeb-4a84-a1d1-bb4a791df15d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## calculating maximum length of the sequence\n",
        "max_len = max([len(seq) for seq in data['Sequence']])\n",
        "print(f\"maximum length = {max_len}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cYgPORQmuZR",
        "outputId": "f32530f2-ca11-417e-e9ba-71ba849b9147"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "maximum length = 37971\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Function for making all sequences into same length\n",
        "def pad_sequence(sequence, padding_size):\n",
        "    padded_sequence = np.zeros(padding_size)\n",
        "    padded_sequence[:len(sequence)] = sequence\n",
        "    return padded_sequence"
      ],
      "metadata": {
        "id": "is32ougrm4kO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['padded_sequence'] = data['transformed_sequence'].apply(lambda x: pad_sequence(x, max_len))"
      ],
      "metadata": {
        "id": "cH6UHh9nm6_g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BO0seOrdm82h",
        "outputId": "e8d77c74-45df-43f5-c953-091503fe64f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            Sequence strain_name  label  \\\n",
              "0  ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...   sars-cov2      0   \n",
              "1  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "2  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "3  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "4  TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...   sars-cov2      0   \n",
              "\n",
              "  label_names                               transformed_sequence  \\\n",
              "0   SARS-CoV2  [1, 2, 2, 1, 1, 1, 4, 4, 2, 2, 2, 1, 2, 1, 3, ...   \n",
              "1   SARS-CoV2  [2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...   \n",
              "2   SARS-CoV2  [2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...   \n",
              "3   SARS-CoV2  [2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...   \n",
              "4   SARS-CoV2  [2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...   \n",
              "\n",
              "                                     padded_sequence  \n",
              "0  [1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 4.0, 4.0, 2.0, ...  \n",
              "1  [2.0, 3.0, 4.0, 2.0, 1.0, 3.0, 4.0, 2.0, 4.0, ...  \n",
              "2  [2.0, 3.0, 4.0, 2.0, 1.0, 3.0, 4.0, 2.0, 4.0, ...  \n",
              "3  [2.0, 3.0, 4.0, 2.0, 1.0, 3.0, 4.0, 2.0, 4.0, ...  \n",
              "4  [2.0, 3.0, 4.0, 2.0, 1.0, 3.0, 4.0, 2.0, 4.0, ...  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-6dfefb59-9397-4571-aefd-ec50cdb5958d\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sequence</th>\n",
              "      <th>strain_name</th>\n",
              "      <th>label</th>\n",
              "      <th>label_names</th>\n",
              "      <th>transformed_sequence</th>\n",
              "      <th>padded_sequence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ATTAAAGGTTTATACCTTCCCAGGTAACAAACCAACCAACTTTCGA...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "      <td>[1, 2, 2, 1, 1, 1, 4, 4, 2, 2, 2, 1, 2, 1, 3, ...</td>\n",
              "      <td>[1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 4.0, 4.0, 2.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "      <td>[2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...</td>\n",
              "      <td>[2.0, 3.0, 4.0, 2.0, 1.0, 3.0, 4.0, 2.0, 4.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "      <td>[2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...</td>\n",
              "      <td>[2.0, 3.0, 4.0, 2.0, 1.0, 3.0, 4.0, 2.0, 4.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "      <td>[2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...</td>\n",
              "      <td>[2.0, 3.0, 4.0, 2.0, 1.0, 3.0, 4.0, 2.0, 4.0, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>TCGTACGTGGCTTTGGAGACTCCGTGGAGGAGGTCTTATCAGAGGC...</td>\n",
              "      <td>sars-cov2</td>\n",
              "      <td>0</td>\n",
              "      <td>SARS-CoV2</td>\n",
              "      <td>[2, 3, 4, 2, 1, 3, 4, 2, 4, 4, 3, 2, 2, 2, 4, ...</td>\n",
              "      <td>[2.0, 3.0, 4.0, 2.0, 1.0, 3.0, 4.0, 2.0, 4.0, ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6dfefb59-9397-4571-aefd-ec50cdb5958d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-5ca2e9f0-f81b-4478-951e-c73e278a063f\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5ca2e9f0-f81b-4478-951e-c73e278a063f')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-5ca2e9f0-f81b-4478-951e-c73e278a063f button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6dfefb59-9397-4571-aefd-ec50cdb5958d 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-6dfefb59-9397-4571-aefd-ec50cdb5958d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jidb0r9Um_oe",
        "outputId": "19d37cfc-6911-482b-8df6-eddc18b0b528"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1781, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['padded_sequence'].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hkVwe20vnBT6",
        "outputId": "dceb9c7a-b8ec-4da0-ab3f-bfd8459a993f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1781,)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### making a new dataframe\n",
        "new_df = pd.DataFrame(data['padded_sequence'].to_list())\n",
        "### saving the preprocessed data into 'genome_sequence.csv'\n",
        "new_df.to_csv('genome_sequence.csv')"
      ],
      "metadata": {
        "id": "JoRpfXw2nC0R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = data['label']\n",
        "label.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEc90TmBnEiM",
        "outputId": "7d125e19-1162-4091-da5a-d6078f325193"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1781,)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### saving the labels as 'multi_level.csv'\n",
        "label.to_csv('multi_label.csv', index=False)"
      ],
      "metadata": {
        "id": "CSIYu4ydneW2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Implementation starts from here"
      ],
      "metadata": {
        "id": "DysPuEWtngHn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "IpPQfmqjn2-H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('genome_sequence.csv')\n",
        "labels = pd.read_csv('multi_label.csv')\n",
        "print(f\"data.shape = {data.shape}\")\n",
        "print(f\"labels.shape = {labels.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIuJbAFbn430",
        "outputId": "1cc86bac-2010-44bd-a55c-924040e12c4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data.shape = (1781, 37972)\n",
            "labels.shape = (1781, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "s-eZXgkAn72g",
        "outputId": "19d5ac7d-d537-4817-f46a-061c6cb7fa7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    <div 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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              "<p>5 rows × 37972 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07396948-024a-4611-af6c-1d0a78c79e95')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-b30a0312-69d3-46d8-b830-c2d9ab98555c\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b30a0312-69d3-46d8-b830-c2d9ab98555c')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-b30a0312-69d3-46d8-b830-c2d9ab98555c button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-07396948-024a-4611-af6c-1d0a78c79e95 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-07396948-024a-4611-af6c-1d0a78c79e95');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "G95Mzau4pCDV",
        "outputId": "fabeb646-60e0-4408-89e2-09f83c0d8156"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   label\n",
              "0      0\n",
              "1      0\n",
              "2      0\n",
              "3      0\n",
              "4      0"
            ],
            "text/html": [
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-392c5bd5-14c6-4a68-81b9-7c8b09b8594f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d11bdc23-12ce-4254-826c-29e7b0ed7aa5')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-d11bdc23-12ce-4254-826c-29e7b0ed7aa5 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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-convert {\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-392c5bd5-14c6-4a68-81b9-7c8b09b8594f 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-392c5bd5-14c6-4a68-81b9-7c8b09b8594f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.countplot(x = labels['label'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "d2I9pAuApDyE",
        "outputId": "16a46de3-8da8-41f9-9c09-efd7c1452ba6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='label', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 112
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## checking the type of the data\n",
        "type(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kMONePGCpF3e",
        "outputId": "309f47cc-a0b6-44db-9122-49ceeed4d83f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6E_AOFEHpHuA",
        "outputId": "fe08be7c-2f2a-4b66-87e1-36cf785b6020"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Converting data and labels into numpy"
      ],
      "metadata": {
        "id": "qyLqsMIgpJTC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.to_numpy()\n",
        "y = labels.to_numpy()"
      ],
      "metadata": {
        "id": "j5BNuK3NpP3E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5MnWxS6EpRqV",
        "outputId": "c46b11c2-a14d-4b5e-ac93-3c04973f43de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1781, 37972)"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hHut43r3pTOz",
        "outputId": "cf08a07c-6f50-43d6-dadd-2bb529ff5f9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1781, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## reshaping the 'labels' array\n",
        "y = y.reshape(-1, )"
      ],
      "metadata": {
        "id": "spdZqYNWpVAS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stDn4zwnpWh1",
        "outputId": "c393a1af-b98f-436c-e57a-6aa73768dd52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1781,)"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_n6DRtS2pYZz",
        "outputId": "eda79936-e94a-4b7a-d8b5-1e09c43b4416"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## target data is X and label is y\n"
      ],
      "metadata": {
        "id": "NLmSrFBSpZyn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"X.shape = {X.shape}\")\n",
        "print(f\"X[0] = {X[0].shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQVNoHPHpeUj",
        "outputId": "1d24545b-3a5e-4c06-acb3-71f6f455c5f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X.shape = (1781, 37972)\n",
            "X[0] = (37972,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Reshaping the data\n",
        "X_data_reshaped = X.reshape(X.shape[0], X.shape[1], 1)\n",
        "X_data_reshaped.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDea_XesphEC",
        "outputId": "b3191225-cf6e-4916-d593-e8f5e65218a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1781, 37972, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_data_reshaped.shape[1:]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J2a-MAwkpihB",
        "outputId": "aa95a18b-8e7a-438a-a35b-3b2368ed1b22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(37972, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## spiltting the dataset into train and test data"
      ],
      "metadata": {
        "id": "NB4eeAicpkD5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "1ZJV1rQLppNW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X_data_reshaped, y, test_size = 0.2, random_state=42, shuffle=True)"
      ],
      "metadata": {
        "id": "GfOgnafgpqtb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FvjYW3ppsMj",
        "outputId": "86d3e879-5dbb-4282-a48e-ec9e2c7aaf3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1424, 37972, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdfhUr0Zpt4s",
        "outputId": "7d246bf7-5a4e-42e5-db55-7b7280b7de15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(357, 37972, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training and evaluating using 1 dimensional convnet"
      ],
      "metadata": {
        "id": "yFHtyBvDpvNI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import models\n",
        "from keras.models import Sequential\n",
        "from keras import layers\n",
        "from keras.optimizers import RMSprop"
      ],
      "metadata": {
        "id": "0xZLNn84p0r9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = models.Sequential([\n",
        "    layers.Conv1D(64, kernel_size=150, activation='relu', input_shape=(X_data_reshaped.shape[1:])),\n",
        "    layers.MaxPooling1D(pool_size=5),\n",
        "    layers.Conv1D(128, 50, activation='relu'),\n",
        "    layers.MaxPooling1D(pool_size=2),\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(7, activation='softmax')\n",
        "])\n",
        "\n",
        "cnn.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])\n",
        "cnn.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xdmo7VhCp1Fy",
        "outputId": "5202153b-cbc3-404e-d3d7-e55616a8b34a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 37823, 64)         9664      \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 7564, 64)         0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 7515, 128)         409728    \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 3757, 128)        0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 480896)            0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 7)                 3366279   \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,785,671\n",
            "Trainable params: 3,785,671\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = cnn.fit(X_train, y_train,\n",
        "                  epochs=10,\n",
        "                  batch_size=256,\n",
        "                  validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNaa1Lj0p2-d",
        "outputId": "ccfce823-9c86-45a6-d357-01e5ddbd0b5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "6/6 [==============================] - 35s 2s/step - loss: 20.5877 - acc: 0.2147 - val_loss: 2.0615 - val_acc: 0.2657\n",
            "Epoch 2/10\n",
            "6/6 [==============================] - 5s 862ms/step - loss: 1.5033 - acc: 0.5324 - val_loss: 0.9070 - val_acc: 0.8881\n",
            "Epoch 3/10\n",
            "6/6 [==============================] - 5s 853ms/step - loss: 0.5588 - acc: 0.8767 - val_loss: 0.4436 - val_acc: 0.8531\n",
            "Epoch 4/10\n",
            "6/6 [==============================] - 5s 887ms/step - loss: 0.2790 - acc: 0.9219 - val_loss: 0.6628 - val_acc: 0.8042\n",
            "Epoch 5/10\n",
            "6/6 [==============================] - 5s 886ms/step - loss: 0.3153 - acc: 0.9219 - val_loss: 0.5989 - val_acc: 0.9021\n",
            "Epoch 6/10\n",
            "6/6 [==============================] - 5s 884ms/step - loss: 0.2720 - acc: 0.9524 - val_loss: 0.3193 - val_acc: 0.9231\n",
            "Epoch 7/10\n",
            "6/6 [==============================] - 5s 861ms/step - loss: 0.1485 - acc: 0.9477 - val_loss: 0.2333 - val_acc: 0.9301\n",
            "Epoch 8/10\n",
            "6/6 [==============================] - 5s 856ms/step - loss: 0.0929 - acc: 0.9688 - val_loss: 0.1857 - val_acc: 0.9510\n",
            "Epoch 9/10\n",
            "6/6 [==============================] - 6s 928ms/step - loss: 0.0866 - acc: 0.9703 - val_loss: 0.1889 - val_acc: 0.9580\n",
            "Epoch 10/10\n",
            "6/6 [==============================] - 6s 910ms/step - loss: 0.0778 - acc: 0.9750 - val_loss: 0.1674 - val_acc: 0.9650\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_, acc = cnn.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyhGewu0p69C",
        "outputId": "87c50b96-69a1-4d04-cd4c-96d55e81e15c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 2s 55ms/step - loss: 0.2422 - acc: 0.9580\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### function for plotting results\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_result(history):\n",
        "\n",
        "    history_dict = history.history\n",
        "#     print(history_dict.keys())\n",
        "\n",
        "    acc = history_dict['acc']\n",
        "    val_acc = history_dict['val_acc']\n",
        "    loss = history_dict['loss']\n",
        "    val_loss = history_dict['val_loss']\n",
        "\n",
        "    epochs = range(1, len(acc) + 1)\n",
        "\n",
        "    plt.plot(epochs, acc, 'orange', label='Training acc')\n",
        "    plt.plot(epochs, val_acc, 'blue', label='Validation acc')\n",
        "    plt.title('Training and Validation accuracy')\n",
        "    plt.legend()\n",
        "    plt.figure()\n",
        "\n",
        "    plt.plot(epochs, loss, 'orange', label='Training loss')\n",
        "    plt.plot(epochs, val_loss, 'blue', label='Validation loss')\n",
        "    plt.legend()\n",
        "\n",
        "\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "kVHrC1FrqR7g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_result(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "4Z0j00MYqZUP",
        "outputId": "9ed28b2f-7736-44d8-8531-7503c9f8552a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### calculating 'y_predicted'\n",
        "y_predicted = cnn.predict(X_test)\n",
        "y_predicted[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2fhliOCeqb1I",
        "outputId": "27c3f86a-f69f-4806-dc50-78206c2ced48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 1s 48ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.0477955e-08, 9.9998915e-01, 6.9714741e-07, 1.8778793e-07,\n",
              "       2.7137227e-07, 8.5593447e-06, 1.1946076e-06], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(y_predicted[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "loCNA0Qgqggy",
        "outputId": "374eabda-ab3f-4230-d21e-72cbde1bad71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted_labels = [np.argmax(i) for i in y_predicted]"
      ],
      "metadata": {
        "id": "sCa-h9TTqifx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted_labels[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_7bHHA4qkLf",
        "outputId": "e3f3488c-4637-41b0-b628-27776a2a15b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 6, 0, 1, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "cm = tf.math.confusion_matrix(labels = y_test, predictions = y_predicted_labels)\n",
        "cm"
      ],
      "metadata": {
        "id": "cB8wwFmfql23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ee5cede-d66e-4210-c571-5a648c875f80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(7, 7), dtype=int32, numpy=\n",
              "array([[ 76,   1,   0,   0,   0,   0,   0],\n",
              "       [  2, 104,   0,   0,   0,   1,   0],\n",
              "       [  0,   0,  15,   0,   0,   1,   1],\n",
              "       [  0,   0,   0,  14,   1,   1,   0],\n",
              "       [  0,   0,   0,   1,  24,   0,   0],\n",
              "       [  0,   1,   0,   1,   0,  38,   1],\n",
              "       [  0,   0,   0,   0,   1,   2,  71]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting confusion matrix"
      ],
      "metadata": {
        "id": "Iv3xvYsqqoW5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "covid_variants = ['sars-cov2', 'mers-cov', 'hCov-OC43', 'hCov-229E', 'hCov-NL63', 'hCov-hkU1', 'sars-cov']"
      ],
      "metadata": {
        "id": "QvVHhrMDqsi5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## plotting confusion matrix\n",
        "import seaborn as sn\n",
        "df_cm = pd.DataFrame(cm, index = [i for i in covid_variants],\n",
        "                  columns = [i for i in covid_variants])\n",
        "plt.figure(figsize = (12, 9))\n",
        "sn.heatmap(df_cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n",
        "# plt.savefig(\"CNN\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "B7Ur124fquIm",
        "outputId": "e61bc9ad-0c32-4461-99eb-0d649b706f68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(120.72222222222221, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plotting classification report"
      ],
      "metadata": {
        "id": "GbsLRAb3qvnO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "Qvgr0Teqq2LH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Classification Report: \\n\", classification_report(y_test, y_predicted_labels, target_names = covid_variants))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pffrvJviq3bl",
        "outputId": "868c4505-18b5-401b-a975-21dbd3504b0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "   sars-cov2       0.97      0.99      0.98        77\n",
            "    mers-cov       0.98      0.97      0.98       107\n",
            "   hCov-OC43       1.00      0.88      0.94        17\n",
            "   hCov-229E       0.88      0.88      0.88        16\n",
            "   hCov-NL63       0.92      0.96      0.94        25\n",
            "   hCov-hkU1       0.88      0.93      0.90        41\n",
            "    sars-cov       0.97      0.96      0.97        74\n",
            "\n",
            "    accuracy                           0.96       357\n",
            "   macro avg       0.94      0.94      0.94       357\n",
            "weighted avg       0.96      0.96      0.96       357\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using global maxpooling layer"
      ],
      "metadata": {
        "id": "AnzN7cafq5Qk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cnn2 = models.Sequential([\n",
        "    layers.Conv1D(64, kernel_size=150, activation='relu', input_shape=(X_data_reshaped.shape[1:])),\n",
        "    layers.MaxPooling1D(pool_size=5),\n",
        "    layers.Conv1D(128, 50, activation='relu'),\n",
        "    layers.MaxPooling1D(pool_size=2),\n",
        "    layers.GlobalMaxPooling1D(),\n",
        "    layers.Dense(7, activation='softmax')\n",
        "])\n",
        "\n",
        "cnn2.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['acc'])\n",
        "cnn2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tXLuSjHEq_gp",
        "outputId": "55433036-9327-4149-deb7-3ff9a5093d96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d_2 (Conv1D)           (None, 37823, 64)         9664      \n",
            "                                                                 \n",
            " max_pooling1d_2 (MaxPooling  (None, 7564, 64)         0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " conv1d_3 (Conv1D)           (None, 7515, 128)         409728    \n",
            "                                                                 \n",
            " max_pooling1d_3 (MaxPooling  (None, 3757, 128)        0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " global_max_pooling1d (Globa  (None, 128)              0         \n",
            " lMaxPooling1D)                                                  \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 7)                 903       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 420,295\n",
            "Trainable params: 420,295\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_2 = cnn2.fit(X_train, y_train,\n",
        "                  epochs=15,\n",
        "                  batch_size=256,\n",
        "                  validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOhX452zrECQ",
        "outputId": "fc8fa7eb-5e2f-4c52-8821-387a0de6d4d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "6/6 [==============================] - 6s 934ms/step - loss: 0.3756 - acc: 0.8548 - val_loss: 0.4034 - val_acc: 0.8392\n",
            "Epoch 2/15\n",
            "6/6 [==============================] - 6s 914ms/step - loss: 0.3382 - acc: 0.8977 - val_loss: 0.3554 - val_acc: 0.9720\n",
            "Epoch 3/15\n",
            "6/6 [==============================] - 6s 938ms/step - loss: 0.3085 - acc: 0.9664 - val_loss: 0.3525 - val_acc: 0.9301\n",
            "Epoch 4/15\n",
            "6/6 [==============================] - 6s 921ms/step - loss: 0.2896 - acc: 0.9586 - val_loss: 0.3230 - val_acc: 0.9720\n",
            "Epoch 5/15\n",
            "6/6 [==============================] - 6s 914ms/step - loss: 0.2749 - acc: 0.9750 - val_loss: 0.2985 - val_acc: 0.9860\n",
            "Epoch 6/15\n",
            "6/6 [==============================] - 6s 939ms/step - loss: 0.2546 - acc: 0.9828 - val_loss: 0.4058 - val_acc: 0.8322\n",
            "Epoch 7/15\n",
            "6/6 [==============================] - 6s 950ms/step - loss: 0.4259 - acc: 0.8134 - val_loss: 0.4204 - val_acc: 0.8671\n",
            "Epoch 8/15\n",
            "6/6 [==============================] - 6s 910ms/step - loss: 0.2989 - acc: 0.9009 - val_loss: 0.2851 - val_acc: 0.9510\n",
            "Epoch 9/15\n",
            "6/6 [==============================] - 6s 902ms/step - loss: 0.2692 - acc: 0.9290 - val_loss: 0.2876 - val_acc: 0.9371\n",
            "Epoch 10/15\n",
            "6/6 [==============================] - 6s 922ms/step - loss: 0.2151 - acc: 0.9672 - val_loss: 0.2375 - val_acc: 0.9860\n",
            "Epoch 11/15\n",
            "6/6 [==============================] - 6s 921ms/step - loss: 0.2604 - acc: 0.9407 - val_loss: 0.4963 - val_acc: 0.7832\n",
            "Epoch 12/15\n",
            "6/6 [==============================] - 5s 888ms/step - loss: 0.6008 - acc: 0.7963 - val_loss: 0.4932 - val_acc: 0.7483\n",
            "Epoch 13/15\n",
            "6/6 [==============================] - 5s 883ms/step - loss: 0.4783 - acc: 0.7767 - val_loss: 0.2824 - val_acc: 0.9441\n",
            "Epoch 14/15\n",
            "6/6 [==============================] - 5s 881ms/step - loss: 0.2425 - acc: 0.9454 - val_loss: 0.3304 - val_acc: 0.9021\n",
            "Epoch 15/15\n",
            "6/6 [==============================] - 5s 880ms/step - loss: 0.2658 - acc: 0.9048 - val_loss: 0.2213 - val_acc: 0.9860\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_, acc_2 = cnn2.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKrvNkWirGio",
        "outputId": "e58d0aac-a586-45c8-b3b8-7c1057e7e408"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 1s 40ms/step - loss: 0.2373 - acc: 0.9636\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_result(history_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "bNWp3DvarbHf",
        "outputId": "c4991b4f-5068-4bc8-d054-f2db9ff36060"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### calculating 'y_predicted'\n",
        "y_predicted2 = cnn2.predict(X_test)\n",
        "y_predicted2[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDI1vtnUrd7g",
        "outputId": "92f812e3-f567-4936-9bdd-b29e1ff1599f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 1s 40ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5.5551245e-05, 9.4298524e-01, 3.8705628e-02, 1.3207760e-02,\n",
              "       4.0457058e-03, 8.7246601e-04, 1.2770348e-04], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted_labels2 = [np.argmax(i) for i in y_predicted2]\n",
        "y_predicted_labels[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMHgK_EirrAq",
        "outputId": "8f33051f-6966-451e-a1e7-c936485a15dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 6, 0, 1, 1]"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm2 = tf.math.confusion_matrix(labels = y_test, predictions = y_predicted_labels2)\n",
        "cm2"
      ],
      "metadata": {
        "id": "8W6GV6NTrxmw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eb76d9fb-11fb-46ae-b81a-fdf5599be024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(7, 7), dtype=int32, numpy=\n",
              "array([[ 77,   0,   0,   0,   0,   0,   0],\n",
              "       [  2, 103,   2,   0,   0,   0,   0],\n",
              "       [  0,   0,  16,   1,   0,   0,   0],\n",
              "       [  0,   0,   1,  14,   0,   1,   0],\n",
              "       [  0,   0,   0,   0,  22,   2,   1],\n",
              "       [  0,   0,   0,   0,   0,  40,   1],\n",
              "       [  0,   0,   0,   0,   0,   2,  72]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting confusion matrix"
      ],
      "metadata": {
        "id": "5rilrAHjr8IL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## plotting confusion matrix\n",
        "import seaborn as sn\n",
        "df_cm2 = pd.DataFrame(cm2, index = [i for i in covid_variants],\n",
        "                  columns = [i for i in covid_variants])\n",
        "plt.figure(figsize = (12, 9))\n",
        "sn.heatmap(df_cm2, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')\n",
        "# plt.savefig(\"CNN\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "VOhDSru3r_05",
        "outputId": "5c92034e-4d17-425a-e689-ee68f78336d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(120.72222222222221, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 158
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Classification Report: \\n\", classification_report(y_test, y_predicted_labels2, target_names = covid_variants))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1IZUF-5qsF0N",
        "outputId": "9328d2c0-bf48-4e56-e649-67b02d408710"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report: \n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "   sars-cov2       0.97      1.00      0.99        77\n",
            "    mers-cov       1.00      0.96      0.98       107\n",
            "   hCov-OC43       0.84      0.94      0.89        17\n",
            "   hCov-229E       0.93      0.88      0.90        16\n",
            "   hCov-NL63       1.00      0.88      0.94        25\n",
            "   hCov-hkU1       0.89      0.98      0.93        41\n",
            "    sars-cov       0.97      0.97      0.97        74\n",
            "\n",
            "    accuracy                           0.96       357\n",
            "   macro avg       0.94      0.94      0.94       357\n",
            "weighted avg       0.97      0.96      0.96       357\n",
            "\n"
          ]
        }
      ]
    }
  ]
}