Giovanni_&_Oscar_Milestone_2.ipynb

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        "id": "FWM79lj2LYI6"
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      "source": [
        "# Giovanni & Oscar | ML Final Project | Milestone 2\n",
        "\n",
        "Setup and Baseline, Due 3/30\n",
        "\n",
        "## Points (10 pts)\n",
        "## Tasks:\n",
        "\n",
        "1. Load the data in a colab notebook\n",
        "\n",
        "2. Split the training data into 90% for training and 10% for validation (your experiments).\n",
        "    > **Note:** You will want to run experiments in your colab notebook without always submitting your test predictions to kaggle.\n",
        "\n",
        "3. Figure out how to submit a baseline system to kaggle that makes predictions on the test data. You should receive a score back from kaggle and see your position on the leaderboard.\n",
        "\n",
        "4. Submit a copy of your colab in Classroom that includes your baseline score evaluated on\n",
        "\n",
        "  - your validation data and\n",
        "  - on the official leaderboard."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WPFnCCd7O6fN"
      },
      "source": [
        "### 0. Setup Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QiwopORsO-yC",
        "outputId": "fa56efa0-7ab1-4f76-9319-137ec3d158f8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pip in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (23.0.1)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "# Ensure that we have the newest version of pip installed\n",
        "%pip install --upgrade pip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pn1OWO_1PRrO",
        "outputId": "f480ba71-6926-41c5-bd42-0fbbf008eb6d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using pip 23.0.1 from c:\\Users\\river\\anaconda3\\envs\\Google-Machine-Learning-Final\\lib\\site-packages\\pip (python 3.10)Note: you may need to restart the kernel to use updated packages.\n",
            "\n",
            "Requirement already satisfied: numpy in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (1.24.2)\n",
            "Using pip 23.0.1 from c:\\Users\\river\\anaconda3\\envs\\Google-Machine-Learning-Final\\lib\\site-packages\\pip (python 3.10)\n",
            "Requirement already satisfied: pandas in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (1.5.3)\n",
            "Requirement already satisfied: pytz>=2020.1 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from pandas) (2022.7.1)\n",
            "Requirement already satisfied: numpy>=1.21.0 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from pandas) (1.24.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Using pip 23.0.1 from c:\\Users\\river\\anaconda3\\envs\\Google-Machine-Learning-Final\\lib\\site-packages\\pip (python 3.10)\n",
            "Requirement already satisfied: matplotlib in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (3.7.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib) (9.4.0)\n",
            "Requirement already satisfied: cycler>=0.10 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib) (4.39.2)\n",
            "Requirement already satisfied: numpy>=1.20 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib) (1.24.2)\n",
            "Requirement already satisfied: packaging>=20.0 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib) (23.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib) (1.0.7)\n",
            "Requirement already satisfied: six>=1.5 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Using pip 23.0.1 from c:\\Users\\river\\anaconda3\\envs\\Google-Machine-Learning-Final\\lib\\site-packages\\pip (python 3.10)\n",
            "Requirement already satisfied: seaborn in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (0.12.2)\n",
            "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from seaborn) (3.7.1)\n",
            "Requirement already satisfied: pandas>=0.25 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from seaborn) (1.5.3)\n",
            "Requirement already satisfied: numpy!=1.24.0,>=1.17 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from seaborn) (1.24.2)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.39.2)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.4.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.7)\n",
            "Requirement already satisfied: cycler>=0.10 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
            "Requirement already satisfied: packaging>=20.0 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
            "Requirement already satisfied: pytz>=2020.1 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from pandas>=0.25->seaborn) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in c:\\users\\river\\anaconda3\\envs\\google-machine-learning-final\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "# Install necessary libraries\n",
        "%pip install -v numpy\n",
        "%pip install -v pandas\n",
        "%pip install -v matplotlib\n",
        "%pip install -v seaborn\n",
        "\n",
        "# Helps avoid showing plots in a separate line\n",
        "# %matplotlib inline\n",
        "\n",
        "# %pip install -v scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "ycGSQ31nQRa8"
      },
      "outputs": [],
      "source": [
        "# Import the modules\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn import datasets\n",
        "\n",
        "# Set the styling of the plt plots to darkgrid\n",
        "sns.set(style='darkgrid')\n",
        "\n",
        "# Removes error messsages and sets precision to 3 decimal places\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "np.set_printoptions(precision=3, suppress=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJCd5JzUNNuk"
      },
      "source": [
        "### 1. Load the Data in a Colab Notebook"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Ct512u3DuP7E"
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              "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
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              "      <th>4</th>\n",
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            "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  "
            ]
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          "output_type": "display_data"
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      ],
      "source": [
        "# Please update the Path here to the location of your train.csv and test.csv files\n",
        "path_to_csv = 'Data/techexchange-2023-ml-project'\n",
        "\n",
        "# Load the Data Frames from the Training and Testing Data Frame\n",
        "train_df = pd.read_csv(f'{path_to_csv}/train.csv')\n",
        "test_df = pd.read_csv(f'{path_to_csv}/test.csv')\n",
        "display(train_df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
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              "</style>\n",
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              "  <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",
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              "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
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              "      <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",
              "    </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",
              "    </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",
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              "    <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",
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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 ..."
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              "      <th></th>\n",
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              "   target\n",
              "0       1\n",
              "1       1\n",
              "2       1\n",
              "3       1\n",
              "4       1"
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      ],
      "source": [
        "# Separate Data Frame for input and outputs\n",
        "\n",
        "input_names = ['id', 'keyword', 'location', 'text']\n",
        "input_df = train_df[input_names]\n",
        "display(input_df.head())\n",
        "\n",
        "output_names = ['target']\n",
        "output_df = train_df[output_names]\n",
        "display(output_df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[1 nan nan\n",
            "  'Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all']\n",
            " [4 nan nan 'Forest fire near La Ronge Sask. Canada']\n",
            " [5 nan nan\n",
            "  \"All residents asked to 'shelter in place' are being notified by officers. No other evacuation or shelter in place orders are expected\"]\n",
            " [6 nan nan\n",
            "  '13,000 people receive #wildfires evacuation orders in California ']\n",
            " [7 nan nan\n",
            "  'Just got sent this photo from Ruby #Alaska as smoke from #wildfires pours into a school ']]\n",
            "[1 1 1 1 1]\n",
            "(7613, 4)\n",
            "(7613,)\n"
          ]
        }
      ],
      "source": [
        "# Convert into numpy data\n",
        "X_data = input_df.to_numpy()\n",
        "Y_data = output_df.to_numpy().flatten()\n",
        "\n",
        "print(X_data[:5])\n",
        "print(Y_data[:5])\n",
        "\n",
        "print(X_data.shape)\n",
        "print(Y_data.shape)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 2. Split the training data into 90% training and 10% for validation (your experiments)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into train and test\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X_data, Y_data, train_size=0.90)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Text\n",
            "[2354 'collapsed' 'LiVE MÌ\\x81S'\n",
            " \"\\x89ÛÏ@TheHighFessions: 'My friend came to school blasted...i asked him if he was high he said pancakes then collapsed' -Iowa City High\\x89Û\\x9d\"]\n",
            "\n",
            "Is Natural Disaster?\n",
            "0 No\n",
            "======================\n",
            "\n",
            "Text\n",
            "[7010 'mayhem' nan 'Mayhem is beautiful']\n",
            "\n",
            "Is Natural Disaster?\n",
            "0 No\n",
            "======================\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Going through a few examples in the training split\n",
        "\n",
        "for index in range(2):\n",
        "    print('Text')\n",
        "    print(X_train[index])\n",
        "    print()\n",
        "    \n",
        "    print('Is Natural Disaster?')\n",
        "    print(Y_train[index], 'Yes' if Y_train[index] == 1 else 'No')\n",
        "    print('======================')\n",
        "    print()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### 3. Testing and Submitting a Baseline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Returns a positive result, regardless of the input\n",
        "def baseline_model(text_inputs):\n",
        "    return 1\n",
        "\n",
        "baseline_model_np = np.vectorize(baseline_model, signature='(n) -> ()')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Input:\n",
            "[2354 'collapsed' 'LiVE MÌ\\x81S'\n",
            " \"\\x89ÛÏ@TheHighFessions: 'My friend came to school blasted...i asked him if he was high he said pancakes then collapsed' -Iowa City High\\x89Û\\x9d\"]\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[7010 'mayhem' nan 'Mayhem is beautiful']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[5466 'flames' 'Louisville, KY '\n",
            " 'Vampiro going through the table of flames #UltimaLucha #LuchaUnderground @Elreynetwork http://t.co/Ox6OUw3Yut']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[2227 'chemical%20emergency' nan\n",
            " 'Google Alert: Emergency units simulate a chemical explosion at NU http://t.co/NDgpWYxu6H']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n",
            "Input:\n",
            "[8706 'sinking' nan\n",
            " '?that horrible sinking feeling when you\\x89Ûªve been at home on your phone for a while and you realise its been on 3G this whole time']\n",
            "\n",
            "Output\n",
            "0\n",
            "\n",
            "Prediction\n",
            "1\n",
            "==================\n",
            "\n"
          ]
        }
      ],
      "source": [
        "baseline_predictions_train = baseline_model_np(X_train)\n",
        "\n",
        "for i in range(5):\n",
        "    print('Input:')\n",
        "    print(X_train[i])\n",
        "    print()\n",
        "\n",
        "    print('Output')\n",
        "    print(Y_train[i])\n",
        "    print()\n",
        "\n",
        "    print('Prediction')\n",
        "    print(baseline_predictions_train[i])\n",
        "    print('==================')\n",
        "    print()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Calculates Log Loss\n",
        "def calculate_loss(labels, predictions):\n",
        "    epsilon = 0.000001  # Prevents taking the natural log of non-positive values\n",
        "    ce_values = -labels * np.log(predictions + epsilon) - (1 - labels) * np.log(1 - predictions + epsilon)\n",
        "    loss = ce_values.mean()\n",
        "    return loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Loss: 7.910997663245557\n",
            "Testing Loss: 7.596717297621001\n"
          ]
        }
      ],
      "source": [
        "training_loss = calculate_loss(Y_train, baseline_predictions_train)\n",
        "print('Training Loss:', training_loss)\n",
        "\n",
        "# Run on the validation data\n",
        "baseline_predictions_test = baseline_model_np(X_test)\n",
        "testing_loss = calculate_loss(Y_test, baseline_predictions_test)\n",
        "print('Testing Loss:', testing_loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create the submission CSV file for our Kaggle submission\n",
        "def save_to_submissions_csv(text_inputs, prediction_labels):\n",
        "    print('Generating \"submission.csv\" file...')\n",
        "\n",
        "    # Extract the ids of the text inputs and flatten to a 1D ndarray\n",
        "    test_ids = text_inputs[:,0].flatten()\n",
        "\n",
        "    # Write the submission file and save to 'submission.csv'\n",
        "    np.savetxt(\n",
        "        'submission.csv',\n",
        "        np.rec.fromarrays([test_ids, prediction_labels]),\n",
        "        fmt=['%s', '%d'],\n",
        "        delimiter=',',\n",
        "        header='id,target',\n",
        "        comments=''\n",
        "    )\n",
        "\n",
        "    # Show success!\n",
        "    print('Successfully created \"submission.csv\"')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generating \"submission.csv\" file...\n",
            "Successfully created \"submission.csv\"\n"
          ]
        }
      ],
      "source": [
        "# Reformat the single training dataframe to an input dataframe\n",
        "input_names = ['id', 'keyword', 'location', 'text']\n",
        "test_input_df = test_df[input_names]\n",
        "\n",
        "# Reformat the input dataframe into a numpy array for running through our model\n",
        "test_input_np = test_input_df.to_numpy()\n",
        "\n",
        "# Predict by using the baseline model on the test input and save to a .csv\n",
        "baseline_predictions_test = baseline_model_np(test_input_np)\n",
        "save_to_submissions_csv(test_input_np, baseline_predictions_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>1</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>"
            ],
            "text/plain": [
              "   id  target\n",
              "0   0       1\n",
              "1   2       1\n",
              "2   3       1\n",
              "3   9       1\n",
              "4  11       1"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Look at the first few predictions to ensure things went smoothly\n",
        "pd.read_csv('submission.csv').head()"
      ]
    }
  ],
  "metadata": {
    "colab": {
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      "toc_visible": true
    },
    "kernelspec": {
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