NLP_Project_C_LBradshaw.ipynb

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        "# Import modules & tools\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import confusion_matrix, classification_report, f1_score"
      ]
    },
    {
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      "source": [
        "# Import datasets\n",
        "\n",
        "train_df = pd.read_csv('train.csv')\n",
        "test_df = pd.read_csv('test.csv')"
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      "outputs": []
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              "   id keyword location                                               text  \\\n",
              "0   1     NaN      NaN  Our Deeds are the Reason of this #earthquake M...   \n",
              "1   4     NaN      NaN             Forest fire near La Ronge Sask. Canada   \n",
              "2   5     NaN      NaN  All residents asked to 'shelter in place' are ...   \n",
              "3   6     NaN      NaN  13,000 people receive #wildfires evacuation or...   \n",
              "4   7     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...   \n",
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              "   id keyword location                                               text\n",
              "0   0     NaN      NaN                 Just happened a terrible car crash\n",
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              "    [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-e57d8125-b85f-49ca-b2c6-3a31b4331a27 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-e57d8125-b85f-49ca-b2c6-3a31b4331a27');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f58367c3-1818-4417-91ab-015231672f6b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f58367c3-1818-4417-91ab-015231672f6b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f58367c3-1818-4417-91ab-015231672f6b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df",
              "summary": "{\n  \"name\": \"test_df\",\n  \"rows\": 3263,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3146,\n        \"min\": 0,\n        \"max\": 10875,\n        \"num_unique_values\": 3263,\n        \"samples\": [\n          8051,\n          425,\n          1330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"keyword\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 221,\n        \"samples\": [\n          \"injury\",\n          \"nuclear%20reactor\",\n          \"engulfed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"location\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1602,\n        \"samples\": [\n          \"UAE\",\n          \"Tokio / Tokyo\",\n          \"Texas\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3243,\n        \"samples\": [\n          \"Latest: USA: Huge sinkhole swallows up Brooklyn intersection http://t.co/vspKHg3nZy\",\n          \"I liked a @YouTube video http://t.co/a5YTAw9Vih S.O.S. Rona Guide - The Red Whirlwind\",\n          \"HitchBot travels Europe and greeted with open arms. Gets destroyed after two weeks in america. There's a lesson to be learned here.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.shape, test_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQAlW_ut9i_1",
        "outputId": "acd957bb-be40-4cdf-f113-7331eab8f800"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7613, 5), (3263, 4))"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove unnecessary columns (not relevant to training result, many missing fields)\n",
        "\n",
        "train_df = train_df.drop(columns=['keyword', 'location'])\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9dMlomrk9pck",
        "outputId": "681d4321-2091-4731-8fe1-89eac1adab56"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id                                               text  target\n",
              "0   1  Our Deeds are the Reason of this #earthquake M...       1\n",
              "1   4             Forest fire near La Ronge Sask. Canada       1\n",
              "2   5  All residents asked to 'shelter in place' are ...       1\n",
              "3   6  13,000 people receive #wildfires evacuation or...       1\n",
              "4   7  Just got sent this photo from Ruby #Alaska as ...       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-71ffd40c-3576-4c35-a955-5007a41ae47b\" 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>id</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>All residents asked to 'shelter in place' are ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71ffd40c-3576-4c35-a955-5007a41ae47b')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-71ffd40c-3576-4c35-a955-5007a41ae47b 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-71ffd40c-3576-4c35-a955-5007a41ae47b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-92e9586d-a992-4937-8d26-8b2060ab33bc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-92e9586d-a992-4937-8d26-8b2060ab33bc')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-92e9586d-a992-4937-8d26-8b2060ab33bc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_df",
              "summary": "{\n  \"name\": \"train_df\",\n  \"rows\": 7613,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3137,\n        \"min\": 1,\n        \"max\": 10873,\n        \"num_unique_values\": 7613,\n        \"samples\": [\n          3796,\n          3185,\n          7769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7503,\n        \"samples\": [\n          \"Three Homes Demolished in Unrecognized Arab Village - International Middle East Media Center http://t.co/ik8m4Yi9T4\",\n          \"Reid Lake fire prompts campground evacuation order http://t.co/jBODKM6rBU\",\n          \"FAAN orders evacuation of abandoned aircraft at MMA http://t.co/dEvYbnVXGQ via @todayng\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = test_df.drop(columns=['keyword', 'location'])\n",
        "test_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "F97r58WZ-Itm",
        "outputId": "53c7649a-e51c-4c34-f671-7d026969d040"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id                                               text\n",
              "0   0                 Just happened a terrible car crash\n",
              "1   2  Heard about #earthquake is different cities, s...\n",
              "2   3  there is a forest fire at spot pond, geese are...\n",
              "3   9           Apocalypse lighting. #Spokane #wildfires\n",
              "4  11      Typhoon Soudelor kills 28 in China and Taiwan"
            ],
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f6da7087-91bc-48f7-98c9-7a83002e7041 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df",
              "summary": "{\n  \"name\": \"test_df\",\n  \"rows\": 3263,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3146,\n        \"min\": 0,\n        \"max\": 10875,\n        \"num_unique_values\": 3263,\n        \"samples\": [\n          8051,\n          425,\n          1330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3243,\n        \"samples\": [\n          \"Latest: USA: Huge sinkhole swallows up Brooklyn intersection http://t.co/vspKHg3nZy\",\n          \"I liked a @YouTube video http://t.co/a5YTAw9Vih S.O.S. Rona Guide - The Red Whirlwind\",\n          \"HitchBot travels Europe and greeted with open arms. Gets destroyed after two weeks in america. There's a lesson to be learned here.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.shape, test_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOBFdwXC-SKd",
        "outputId": "96caa1ed-9b1f-4e5d-f143-32192670a126"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((7613, 3), (3263, 2))"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install kgptalkie\n",
        "\n",
        "#!pip install git+https://github.com/laxmimerit/preprocess_kgptalkie.git"
      ],
      "metadata": {
        "id": "FWQMd_RcALnq"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import preprocess_kgptalkie as ps"
      ],
      "metadata": {
        "id": "wr82ghQtAXEL"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean data\n",
        "\n",
        "train_df['text'] = train_df['text'].apply(lambda x: ps.cont_exp(x))\n",
        "train_df['text'] = train_df['text'].apply(lambda x: ps.remove_accented_chars(x))\n",
        "train_df['text'] = train_df['text'].apply(lambda x: ps.remove_emails(x))\n",
        "train_df['text'] = train_df['text'].apply(lambda x: ps.remove_urls(x))\n",
        "train_df['text'] = train_df['text'].apply(lambda x: ps.make_base(x))\n",
        "train_df['text'] = train_df['text'].apply(lambda x: str(x).lower())\n",
        "train_df['text'] = train_df['text'].apply(lambda x: ps.remove_special_chars(x))"
      ],
      "metadata": {
        "id": "orkPSX6c-gmM"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Review real (1) vs metaphorical (0) disaster distribution\n",
        "\n",
        "train_df['target'].value_counts().plot(kind='pie', autopct='%1.1f%%')\n",
        "plt.title('Disaster Tweet - Real or Metaphor ')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "zL5KXyp2CTo6",
        "outputId": "32f1e7f2-37a6-4888-c26e-5dc51e3f867d"
      },
      "execution_count": 80,
      "outputs": [
        {
          "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": [
        "# Check basic features of training dataframe\n",
        "\n",
        "features = ps.get_basic_features(train_df)\n",
        "features.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        },
        "id": "BGqSSQOfDCz3",
        "outputId": "9650956c-a407-49b0-9d10-f6842c7976ae"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id                                               text  target  char_counts  \\\n",
              "0   1  our deed are the reason of this earthquake may...       1           55   \n",
              "1   4              forest fire near la ronge sask canada       1           31   \n",
              "2   5  all resident ask to shelter in place are being...       1          100   \n",
              "3   6  13000 people receive wildfire evacuation order...       1           53   \n",
              "4   7  just get send this photo from ruby alaska as s...       1           68   \n",
              "\n",
              "   word_counts  avg_wordlength  stopwords_counts  hashtag_counts  \\\n",
              "0           13        4.230769                 8               0   \n",
              "1            7        4.428571                 0               0   \n",
              "2           22        4.545455                11               0   \n",
              "3            8        6.625000                 1               0   \n",
              "4           16        4.250000                 8               0   \n",
              "\n",
              "   mentions_counts  digits_counts  uppercase_counts  \n",
              "0                0              0                 0  \n",
              "1                0              0                 0  \n",
              "2                0              0                 0  \n",
              "3                0              1                 0  \n",
              "4                0              0                 0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1872cdbc-1430-481e-875d-890dbca83daa\" 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>id</th>\n",
              "      <th>text</th>\n",
              "      <th>target</th>\n",
              "      <th>char_counts</th>\n",
              "      <th>word_counts</th>\n",
              "      <th>avg_wordlength</th>\n",
              "      <th>stopwords_counts</th>\n",
              "      <th>hashtag_counts</th>\n",
              "      <th>mentions_counts</th>\n",
              "      <th>digits_counts</th>\n",
              "      <th>uppercase_counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>our deed are the reason of this earthquake may...</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>13</td>\n",
              "      <td>4.230769</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>forest fire near la ronge sask canada</td>\n",
              "      <td>1</td>\n",
              "      <td>31</td>\n",
              "      <td>7</td>\n",
              "      <td>4.428571</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5</td>\n",
              "      <td>all resident ask to shelter in place are being...</td>\n",
              "      <td>1</td>\n",
              "      <td>100</td>\n",
              "      <td>22</td>\n",
              "      <td>4.545455</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6</td>\n",
              "      <td>13000 people receive wildfire evacuation order...</td>\n",
              "      <td>1</td>\n",
              "      <td>53</td>\n",
              "      <td>8</td>\n",
              "      <td>6.625000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>just get send this photo from ruby alaska as s...</td>\n",
              "      <td>1</td>\n",
              "      <td>68</td>\n",
              "      <td>16</td>\n",
              "      <td>4.250000</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1872cdbc-1430-481e-875d-890dbca83daa')\"\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
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              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "features",
              "summary": "{\n  \"name\": \"features\",\n  \"rows\": 7613,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3137,\n        \"min\": 1,\n        \"max\": 10873,\n        \"num_unique_values\": 7613,\n        \"samples\": [\n          3796,\n          3185,\n          7769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6913,\n        \"samples\": [\n          \"why do my phone electrocute i when it is charge\",\n          \"well saturn do not exist anymore so the collision place have a starting estimate of 4000 that s 3 time what my car is worth\",\n          \"rainstorm update further north or further south\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"char_counts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 4,\n        \"max\": 135,\n        \"num_unique_values\": 124,\n        \"samples\": [\n          20,\n          63\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"word_counts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 1,\n        \"max\": 34,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          6,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_wordlength\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0363445384527852,\n        \"min\": 1.7,\n        \"max\": 16.0,\n        \"num_unique_values\": 777,\n        \"samples\": [\n          5.266666666666667,\n          4.095238095238095\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stopwords_counts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 0,\n        \"max\": 23,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          6,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hashtag_counts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mentions_counts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"digits_counts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 11,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"uppercase_counts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore best model\n",
        "\n",
        "# Import classification models\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier     # Decision Tree Classification\n",
        "from sklearn.ensemble import RandomForestClassifier # Random Forest Classification\n",
        "from sklearn.linear_model import LogisticRegression # Logistic Regression\n",
        "from sklearn.neighbors import KNeighborsClassifier  # K-Nearest Neighbors\n",
        "from sklearn.svm import SVC                         # Support Vector Machine"
      ],
      "metadata": {
        "id": "NARHD4LGDnF0"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create arrays\n",
        "\n",
        "X = train_df['text']\n",
        "y = train_df['target']"
      ],
      "metadata": {
        "id": "uW8bOY0OH9c7"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Vectorization\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf = TfidfVectorizer(max_features=10000)\n",
        "X = tfidf.fit_transform(X)"
      ],
      "metadata": {
        "id": "cwfRlFtpIKTE"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting data.\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0, stratify=y)"
      ],
      "metadata": {
        "id": "ZMW1aGQ4ITxc"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Models parameters\n",
        "\n",
        "models = {\n",
        "    'Logistic Regression':LogisticRegression(random_state=0),\n",
        "    'K-Nearest Neighbors':KNeighborsClassifier(metric='minkowski', n_neighbors=5, p=2),\n",
        "    'Support Vector Machine':SVC(kernel='linear', random_state=0),\n",
        "    'Kernel Support Vector Machine':SVC(kernel='rbf', random_state=0),\n",
        "    'Decision Tree Classification':DecisionTreeClassifier(criterion='entropy', random_state=0),\n",
        "    'Random Forest Classification':RandomForestClassifier(n_estimators=10, criterion='entropy', random_state=0),\n",
        "}"
      ],
      "metadata": {
        "id": "32Sb5le_Iohn"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model evaluation.\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "for name, model in models.items():\n",
        "  model.fit(X_train, y_train)\n",
        "  y_pred = model.predict(X_test)\n",
        "\n",
        "  print(f'Model: {name}')\n",
        "  print(confusion_matrix(y_test, y_pred))\n",
        "  print(\"Accuracy (single): {:.2f}%\".format(accuracy_score(y_test, y_pred)*100))\n",
        "  accuracies = cross_val_score(estimator=model, X=X_train, y=y_train, cv=10)\n",
        "  print(\"Accuracy (k-fold): {:.2f}%\".format(accuracies.mean()*100))\n",
        "  print(\"Standard Deviation: {:.2f}%\".format(accuracies.std()*100))\n",
        "  print('------------------------------------------------')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7ts7afPIwAA",
        "outputId": "ff500a05-41a4-4689-e32a-467d9f8fe455"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: Logistic Regression\n",
            "[[799  70]\n",
            " [204 450]]\n",
            "Accuracy (single): 82.01%\n",
            "Accuracy (k-fold): 80.21%\n",
            "Standard Deviation: 1.03%\n",
            "------------------------------------------------\n",
            "Model: K-Nearest Neighbors\n",
            "[[859  10]\n",
            " [514 140]]\n",
            "Accuracy (single): 65.59%\n",
            "Accuracy (k-fold): 64.99%\n",
            "Standard Deviation: 0.62%\n",
            "------------------------------------------------\n",
            "Model: Support Vector Machine\n",
            "[[787  82]\n",
            " [195 459]]\n",
            "Accuracy (single): 81.81%\n",
            "Accuracy (k-fold): 80.21%\n",
            "Standard Deviation: 1.49%\n",
            "------------------------------------------------\n",
            "Model: Kernel Support Vector Machine\n",
            "[[804  65]\n",
            " [223 431]]\n",
            "Accuracy (single): 81.09%\n",
            "Accuracy (k-fold): 80.34%\n",
            "Standard Deviation: 1.35%\n",
            "------------------------------------------------\n",
            "Model: Decision Tree Classification\n",
            "[[684 185]\n",
            " [224 430]]\n",
            "Accuracy (single): 73.15%\n",
            "Accuracy (k-fold): 71.58%\n",
            "Standard Deviation: 1.49%\n",
            "------------------------------------------------\n",
            "Model: Random Forest Classification\n",
            "[[799  70]\n",
            " [257 397]]\n",
            "Accuracy (single): 78.53%\n",
            "Accuracy (k-fold): 77.19%\n",
            "Standard Deviation: 1.17%\n",
            "------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model accuracy, in order from best model to lesser model\n",
        "\n",
        "# Logistic Regression - 82.01%\n",
        "# SVM - 81.81%\n",
        "# Kernel SVM - 81.09%\n",
        "# Random Forest - 78.53%\n",
        "# Decision Tree - 73.15%\n",
        "# KNN - 65.59%"
      ],
      "metadata": {
        "id": "vrDM9c11I4-S"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Training with logistic regression\n",
        "\n",
        "# Setting data arrays.\n",
        "X_train = train_df['text']\n",
        "y_train = train_df['target']\n",
        "\n",
        "X_test = test_df['text']\n",
        "\n",
        "# Applying TF-IDF vectorization to data.\n",
        "tfidf = TfidfVectorizer(max_features=10000)\n",
        "X_train = tfidf.fit_transform(X_train)\n",
        "X_test = tfidf.transform(X_test)\n",
        "\n",
        "# Train the model.\n",
        "model = LogisticRegression(C=1, penalty='l2', solver='lbfgs', random_state=0)\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "# Predict the data.\n",
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "id": "TGY6P6oqKUUF"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Export Results\n",
        "\n",
        "results = pd.DataFrame({\n",
        "    'id': test_df['id'],\n",
        "    'target': y_pred\n",
        "})\n",
        "\n",
        "results.to_csv('results.csv', index=False)"
      ],
      "metadata": {
        "id": "X7LRNh4ZKiri"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "H2vxMX7bMRjn",
        "outputId": "54856390-3eaf-498b-d023-a490cd354629"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   id  target\n",
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              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9fdca89a-c0e8-4ba8-be71-76ac8462ea8e')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-9fdca89a-c0e8-4ba8-be71-76ac8462ea8e 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-9fdca89a-c0e8-4ba8-be71-76ac8462ea8e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-43c011fe-0fde-4164-921b-2b25c5d3acd5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-43c011fe-0fde-4164-921b-2b25c5d3acd5')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-43c011fe-0fde-4164-921b-2b25c5d3acd5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 3263,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3146,\n        \"min\": 0,\n        \"max\": 10875,\n        \"num_unique_values\": 3263,\n        \"samples\": [\n          8051,\n          425,\n          1330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FfjONYDoNCFZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}