{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "d3afe111",
      "metadata": {
        "id": "d3afe111"
      },
      "source": [
        "# Disaster Tweet Classification"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ff61e4a8",
      "metadata": {
        "id": "ff61e4a8"
      },
      "source": [
        "Machine Learning Club @SJSU -\n",
        "Authors: Robert Yav, Richard Ky"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "00c7e16c",
      "metadata": {
        "id": "00c7e16c"
      },
      "source": [
        "The dataset used in this project is a dataset taken from kaggle: https://www.kaggle.com/code/cyrillescolano/nlp-disaster-tweet-classification The purpose of this project is to construct a ML model that will as accurately as possible classify tweets into disaster and nondisaster tweets.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "5d6531d6",
      "metadata": {
        "id": "5d6531d6"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "3c45d4db",
      "metadata": {
        "id": "3c45d4db",
        "outputId": "38bfb6c3-47ee-417b-b390-babbfeaa90a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# Importing data (will be different as we work in different environments)\n",
        "# In this case, this is Robert's Branch\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "test= pd.read_csv(\"/content/drive/MyDrive/DisasterClassification/data/test.csv\")\n",
        "train= pd.read_csv(\"/content/drive/MyDrive/DisasterClassification/data/train.csv\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2eecfdf8",
      "metadata": {
        "id": "2eecfdf8"
      },
      "source": [
        "We will begin with exploratory data analysis on the training dataset in order to identify any patterns or issues early on before the modeling stage."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "b8e18219",
      "metadata": {
        "id": "b8e18219",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "534971d4-e53a-4999-b83e-c29996f04e3e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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",
              "\n",
              "   target  \n",
              "0       1  \n",
              "1       1  \n",
              "2       1  \n",
              "3       1  \n",
              "4       1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-efde985e-922f-4e88-a829-90a773352901\">\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>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>location</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>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</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",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efde985e-922f-4e88-a829-90a773352901')\"\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",
              "  <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-efde985e-922f-4e88-a829-90a773352901 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-efde985e-922f-4e88-a829-90a773352901');\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": 3
        }
      ],
      "source": [
        "train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "bd124960",
      "metadata": {
        "id": "bd124960",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "cd3f28c7-da1e-4fd4-976a-3f8d80b957c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id keyword location                                               text\n",
              "0   0     NaN      NaN                 Just happened a terrible car crash\n",
              "1   2     NaN      NaN  Heard about #earthquake is different cities, s...\n",
              "2   3     NaN      NaN  there is a forest fire at spot pond, geese are...\n",
              "3   9     NaN      NaN           Apocalypse lighting. #Spokane #wildfires\n",
              "4  11     NaN      NaN      Typhoon Soudelor kills 28 in China and Taiwan"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-00d1c002-3da4-4927-a59e-a6d5900fa2d4\">\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>id</th>\n",
              "      <th>keyword</th>\n",
              "      <th>location</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Just happened a terrible car crash</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Heard about #earthquake is different cities, s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>there is a forest fire at spot pond, geese are...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Apocalypse lighting. #Spokane #wildfires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00d1c002-3da4-4927-a59e-a6d5900fa2d4')\"\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",
              "  <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-00d1c002-3da4-4927-a59e-a6d5900fa2d4 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-00d1c002-3da4-4927-a59e-a6d5900fa2d4');\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": 4
        }
      ],
      "source": [
        "test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "227bb2bc",
      "metadata": {
        "id": "227bb2bc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a49d207-65cc-46c9-d6ab-71f27c198d01"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7613 entries, 0 to 7612\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   id        7613 non-null   int64 \n",
            " 1   keyword   7552 non-null   object\n",
            " 2   location  5080 non-null   object\n",
            " 3   text      7613 non-null   object\n",
            " 4   target    7613 non-null   int64 \n",
            "dtypes: int64(2), object(3)\n",
            "memory usage: 297.5+ KB\n"
          ]
        }
      ],
      "source": [
        "train.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "b8139bad",
      "metadata": {
        "id": "b8139bad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5104ee0f-7c68-4847-8dbc-714f73a18372"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.33272034677525286\n",
            "0.3386454183266932\n"
          ]
        }
      ],
      "source": [
        "print(sum(train[\"location\"].isna())/train[\"location\"].size)\n",
        "print(sum(test[\"location\"].isna())/test[\"location\"].size)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3acd4ed9",
      "metadata": {
        "id": "3acd4ed9"
      },
      "source": [
        "Initially we can see that the location column contains many empty values. (33.27% null in train, 33.86% in test) Imputing this many null values may have a disasterous affect on the data, so we can choose to omit location."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "92324b1b",
      "metadata": {
        "id": "92324b1b"
      },
      "outputs": [],
      "source": [
        "train.drop(columns=[\"location\"],inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"target\"].value_counts()\n",
        "\n",
        "ndisaster0=sum(train[\"target\"]==0)/(sum(train[\"target\"]==0)+sum(train[\"target\"]==1))\n",
        "ndisaster1=sum(train[\"target\"]==1)/(sum(train[\"target\"]==0)+sum(train[\"target\"]==1))\n",
        "plt.pie([ndisaster0,ndisaster1],labels=[\"Disaster: 42.965%\", \"Non-Disaster: 57.03%\"],colors=['xkcd:sky blue', \"green\"])\n",
        "plt.title(\"Number of Disasters vs Non-Disasters\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "Isg-oISHzqKX",
        "outputId": "0f4aa6fe-c22d-42b4-c621-1560d6fb964b"
      },
      "id": "Isg-oISHzqKX",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "8ba50235",
      "metadata": {
        "id": "8ba50235",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b2ab1ed-b3d2-40d8-f2a8-aea72817256c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.008012610009194798\n",
            "0.00796812749003984\n"
          ]
        }
      ],
      "source": [
        "print(sum(train[\"keyword\"].isna())/train[\"keyword\"].size)\n",
        "print(sum(test[\"keyword\"].isna())/test[\"keyword\"].size)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89615427",
      "metadata": {
        "id": "89615427"
      },
      "source": [
        "The only other column containing null values is the column \"keyword\". However, there is less than a percent (~0.8% null in both train and test) of empty values in this column. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "dcbd2a3e",
      "metadata": {
        "id": "dcbd2a3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "063a9ce9-8f29-4827-d929-4ff40f3226dd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id keyword                                               text  target\n",
              "0         1     NaN  Our Deeds are the Reason of this #earthquake M...       1\n",
              "1         4     NaN             Forest fire near La Ronge Sask. Canada       1\n",
              "2         5     NaN  All residents asked to 'shelter in place' are ...       1\n",
              "3         6     NaN  13,000 people receive #wildfires evacuation or...       1\n",
              "4         7     NaN  Just got sent this photo from Ruby #Alaska as ...       1\n",
              "...     ...     ...                                                ...     ...\n",
              "7608  10869     NaN  Two giant cranes holding a bridge collapse int...       1\n",
              "7609  10870     NaN  @aria_ahrary @TheTawniest The out of control w...       1\n",
              "7610  10871     NaN  M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...       1\n",
              "7611  10872     NaN  Police investigating after an e-bike collided ...       1\n",
              "7612  10873     NaN  The Latest: More Homes Razed by Northern Calif...       1\n",
              "\n",
              "[61 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2fba7552-4563-4620-b08a-b4d656adaae4\">\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>id</th>\n",
              "      <th>keyword</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>NaN</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>NaN</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>NaN</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>NaN</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>NaN</td>\n",
              "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
              "      <td>1</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>7608</th>\n",
              "      <td>10869</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Two giant cranes holding a bridge collapse int...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7609</th>\n",
              "      <td>10870</td>\n",
              "      <td>NaN</td>\n",
              "      <td>@aria_ahrary @TheTawniest The out of control w...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7610</th>\n",
              "      <td>10871</td>\n",
              "      <td>NaN</td>\n",
              "      <td>M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7611</th>\n",
              "      <td>10872</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Police investigating after an e-bike collided ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7612</th>\n",
              "      <td>10873</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The Latest: More Homes Razed by Northern Calif...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2fba7552-4563-4620-b08a-b4d656adaae4')\"\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",
              "  <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-2fba7552-4563-4620-b08a-b4d656adaae4 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-2fba7552-4563-4620-b08a-b4d656adaae4');\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": 9
        }
      ],
      "source": [
        "train.loc[train[\"keyword\"].isna()]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "20a593b7",
      "metadata": {
        "id": "20a593b7"
      },
      "source": [
        "We can either choose to omit these entries, generate the model with these entries empty, or fill in the null values manually with our own keywords selected from the text."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "53dc5045",
      "metadata": {
        "id": "53dc5045",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e04d0879-ff68-41d5-8017-5e87bdcc0390"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id keyword                                               text  target\n",
              "0    1     NaN  Our Deeds are the Reason of this #earthquake M...       1\n",
              "1    4     NaN             Forest fire near La Ronge Sask. Canada       1\n",
              "2    5     NaN  All residents asked to 'shelter in place' are ...       1\n",
              "3    6     NaN  13,000 people receive #wildfires evacuation or...       1\n",
              "4    7     NaN  Just got sent this photo from Ruby #Alaska as ...       1\n",
              "5    8     NaN  #RockyFire Update => California Hwy. 20 closed...       1\n",
              "6   10     NaN  #flood #disaster Heavy rain causes flash flood...       1\n",
              "7   13     NaN  I'm on top of the hill and I can see a fire in...       1\n",
              "8   14     NaN  There's an emergency evacuation happening now ...       1\n",
              "9   15     NaN  I'm afraid that the tornado is coming to our a...       1\n",
              "10  16     NaN        Three people died from the heat wave so far       1\n",
              "11  17     NaN  Haha South Tampa is getting flooded hah- WAIT ...       1\n",
              "12  18     NaN  #raining #flooding #Florida #TampaBay #Tampa 1...       1\n",
              "13  19     NaN            #Flood in Bago Myanmar #We arrived Bago       1\n",
              "14  20     NaN  Damage to school bus on 80 in multi car crash ...       1\n",
              "15  23     NaN                                     What's up man?       0\n",
              "16  24     NaN                                      I love fruits       0\n",
              "17  25     NaN                                   Summer is lovely       0\n",
              "18  26     NaN                                  My car is so fast       0\n",
              "19  28     NaN                       What a goooooooaaaaaal!!!!!!       0\n",
              "20  31     NaN                             this is ridiculous....       0\n",
              "21  32     NaN                                  London is cool ;)       0\n",
              "22  33     NaN                                        Love skiing       0\n",
              "23  34     NaN                              What a wonderful day!       0\n",
              "24  36     NaN                                           LOOOOOOL       0\n",
              "25  37     NaN                     No way...I can't eat that shit       0\n",
              "26  38     NaN                              Was in NYC last week!       0\n",
              "27  39     NaN                                 Love my girlfriend       0\n",
              "28  40     NaN                                          Cooool :)       0\n",
              "29  41     NaN                                 Do you like pasta?       0\n",
              "30  44     NaN                                           The end!       0\n",
              "31  48  ablaze  @bbcmtd Wholesale Markets ablaze http://t.co/l...       1\n",
              "32  49  ablaze  We always try to bring the heavy. #metal #RT h...       0\n",
              "33  50  ablaze  #AFRICANBAZE: Breaking news:Nigeria flag set a...       1\n",
              "34  52  ablaze                 Crying out for more! Set me ablaze       0\n",
              "35  53  ablaze  On plus side LOOK AT THE SKY LAST NIGHT IT WAS...       0\n",
              "36  54  ablaze  @PhDSquares #mufc they've built so much hype a...       0\n",
              "37  55  ablaze  INEC Office in Abia Set Ablaze - http://t.co/3...       1\n",
              "38  56  ablaze  Barbados #Bridgetown JAMAICA ‰ÛÒ Two cars set ...       1\n",
              "39  57  ablaze                             Ablaze for you Lord :D       0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc4ef866-6c30-4103-9b69-ee1c76d42b19\">\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>id</th>\n",
              "      <th>keyword</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>NaN</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>NaN</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>NaN</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>NaN</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>NaN</td>\n",
              "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "      <td>#RockyFire Update =&gt; California Hwy. 20 closed...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>#flood #disaster Heavy rain causes flash flood...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I'm on top of the hill and I can see a fire in...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>There's an emergency evacuation happening now ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I'm afraid that the tornado is coming to our a...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Three people died from the heat wave so far</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Haha South Tampa is getting flooded hah- WAIT ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>18</td>\n",
              "      <td>NaN</td>\n",
              "      <td>#raining #flooding #Florida #TampaBay #Tampa 1...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>19</td>\n",
              "      <td>NaN</td>\n",
              "      <td>#Flood in Bago Myanmar #We arrived Bago</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Damage to school bus on 80 in multi car crash ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>23</td>\n",
              "      <td>NaN</td>\n",
              "      <td>What's up man?</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I love fruits</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Summer is lovely</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>26</td>\n",
              "      <td>NaN</td>\n",
              "      <td>My car is so fast</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>28</td>\n",
              "      <td>NaN</td>\n",
              "      <td>What a goooooooaaaaaal!!!!!!</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>this is ridiculous....</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>London is cool ;)</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>33</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Love skiing</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>What a wonderful day!</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LOOOOOOL</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No way...I can't eat that shit</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>38</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Was in NYC last week!</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>39</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Love my girlfriend</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cooool :)</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>41</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Do you like pasta?</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>The end!</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>48</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>@bbcmtd Wholesale Markets ablaze http://t.co/l...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>49</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>We always try to bring the heavy. #metal #RT h...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>50</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>#AFRICANBAZE: Breaking news:Nigeria flag set a...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>52</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>Crying out for more! Set me ablaze</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>53</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>On plus side LOOK AT THE SKY LAST NIGHT IT WAS...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>54</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>@PhDSquares #mufc they've built so much hype a...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>55</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>INEC Office in Abia Set Ablaze - http://t.co/3...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>56</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>Barbados #Bridgetown JAMAICA ‰ÛÒ Two cars set ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>57</td>\n",
              "      <td>ablaze</td>\n",
              "      <td>Ablaze for you Lord :D</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc4ef866-6c30-4103-9b69-ee1c76d42b19')\"\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",
              "  <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-dc4ef866-6c30-4103-9b69-ee1c76d42b19 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-dc4ef866-6c30-4103-9b69-ee1c76d42b19');\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": 10
        }
      ],
      "source": [
        "train.head(40)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "1ed1938b",
      "metadata": {
        "id": "1ed1938b"
      },
      "outputs": [],
      "source": [
        "#train[\"keyword\"].unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "bcce9685",
      "metadata": {
        "id": "bcce9685"
      },
      "outputs": [],
      "source": [
        "#Note to team: What should we do? Many of the na's can be preperly put into keywords but some are just online phrases like \"LoOOL!!\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "35b4b9d3",
      "metadata": {
        "id": "35b4b9d3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bc8c43d-dbed-4288-81a2-cd38b4a3d3c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "221\n",
            "221\n"
          ]
        }
      ],
      "source": [
        "print(test[\"keyword\"].nunique())\n",
        "print(train[\"keyword\"].nunique()) #221 Unique keywords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "cacc977a",
      "metadata": {
        "id": "cacc977a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9de35f16-1699-4b2b-9157-bd585ce62c47"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x2520 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Gotta find a better way to show the unique values. (main issue is that there are too many. maybe use subplots?)\n",
        "unique_values = train[\"keyword\"].value_counts()\n",
        "fig,ax = plt.subplots(1,1,figsize=(10,35))\n",
        "plt.scatter(unique_values.values,unique_values.index)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "392c5a17",
      "metadata": {
        "id": "392c5a17",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e134cb23-3e98-42e8-f26c-1dac857a8b0a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id          0\n",
              "keyword    61\n",
              "text        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "train.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train[\"text\"]=train[\"text\"].str.lower()\n",
        "#Tokenization step\n",
        "import spacy\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "# Modified tokenizer with help from ChatGPT:\n",
        "# Note: It isn't perfect and needs to be modified more closely.\n",
        "# For example: the @user and random letters (that lose their meaning after being stripped)\n",
        "def spacy_tokenizer(text):\n",
        "    doc = nlp(text)\n",
        "    tokens = [token.lemma_.lower().strip() if token.lemma_ != \"-PRON-\" else token.lower_ for token in doc if not token.is_stop and not token.is_punct]\n",
        "    return tokens\n",
        "train['tokens'] = train['text'].apply(spacy_tokenizer)"
      ],
      "metadata": {
        "id": "dBn3YcBed8ha"
      },
      "id": "dBn3YcBed8ha",
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train['tokens']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMO3k-Jx8EjO",
        "outputId": "f7f4d23e-537b-4e21-c00d-9f2cc9c6e4cd"
      },
      "id": "qMO3k-Jx8EjO",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0              [deed, reason, earthquake, allah, forgive]\n",
              "1           [forest, fire, near, la, ronge, sask, canada]\n",
              "2       [resident, ask, shelter, place, notify, office...\n",
              "3       [13,000, people, receive, wildfire, evacuation...\n",
              "4       [got, send, photo, ruby, alaska, smoke, wildfi...\n",
              "                              ...                        \n",
              "7608    [giant, crane, hold, bridge, collapse, nearby,...\n",
              "7609    [@aria_ahrary, @thetawniest, control, wild, fi...\n",
              "7610    [m1.94, 01:04, utc]?5, km, s, volcano, hawaii,...\n",
              "7611    [police, investigate, e, bike, collide, car, l...\n",
              "7612    [late, home, raze, northern, california, wildf...\n",
              "Name: tokens, Length: 7613, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "9097e5af",
      "metadata": {
        "id": "9097e5af"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "4b62ea8e",
      "metadata": {
        "id": "4b62ea8e"
      },
      "outputs": [],
      "source": [
        "##Splitting the data (Change the test size back to 0.2, we changed it higher to reduce load time)\n",
        "X=train.drop(columns=[\"target\"])\n",
        "y=train[\"target\"]\n",
        "X.shape,y.shape\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,random_state=42, test_size=0.8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "1b7faf8d",
      "metadata": {
        "id": "1b7faf8d"
      },
      "outputs": [],
      "source": [
        "# Attempting to fill in na values by filling them with preexisting keywords.\n",
        "mask1 = X_train.loc[X_train[\"keyword\"].isna()] \n",
        "\n",
        "# Can ignore these, just for debugging\n",
        "#print(mask1['keyword'].isna().sum())  \n",
        "#print(len(mask1[mask1['keyword'] == 'wildfire']))\n",
        "\n",
        "words = X_train['keyword'].unique().astype(np.str_)\n",
        "keywords = np.char.replace(words, \"%20\", ' ')\n",
        "\n",
        "for x in keywords:\n",
        "    # Note to self: must use str.lower() and str.contains() for Pandas series\n",
        "    mask2 = mask1[\"text\"].str.contains(x)\n",
        "    mask1.loc[mask2,\"keyword\"] = x \n",
        "\n",
        "mask1.loc[mask1.keyword.isna(), 'keyword'] = 'none'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Note to self: cannot use regular str with numpy array, must use numpy.str"
      ],
      "metadata": {
        "id": "CwEFmxQxaj3C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4075805-b1af-47b9-a3ee-7787303c6e1b"
      },
      "id": "CwEFmxQxaj3C",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'str'>\n",
            "<class 'numpy.str_'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QlABs6aN4eKX"
      },
      "id": "QlABs6aN4eKX",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}