making-your-first-submission-on-numerai.ipynb

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      "name": "Making your first submission on Numerai",
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        "<a href=\"https://colab.research.google.com/github/numerai/example-scripts/blob/master/making-your-first-submission-on-numerai.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Making your first submission on Numerai\n",
        "\n",
        "## Introduction \n",
        "This tutorial will go over how to create your first submission on Numerai.\n",
        "\n",
        "## Overview\n",
        "\n",
        "1. Using this notebook\n",
        "2. Download the datasets\n",
        "3. Train your first model\n",
        "4. Generate your first predictions\n",
        "4. Make your first submission\n",
        "\n",
        "\n",
        "---\n"
      ]
    },
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        "\n",
        "## 1. Using this notebook \n",
        "\n",
        "This is an interactive notebook. You can execute code in each cell by pressing `shift+enter`. This requires you to login with your Google account.\n",
        "\n",
        "In order to make changes, you need to make a copy by `File -> Save a copy in Drive`.\n",
        "\n",
        "Let's start off by installing and importing our dependencies."
      ]
    },
    {
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      "metadata": {
        "id": "zDKQ_zqwTBf6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# install dependencies\n",
        "!pip install pandas sklearn numerapi"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MaNpcfNkKUg_",
        "colab_type": "code",
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      },
      "source": [
        "# import dependencies\n",
        "import pandas as pd\n",
        "import numerapi\n",
        "import sklearn.linear_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
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        "id": "5uUN-aE_08kW",
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      "source": [
        "## 2. Download the datasets\n",
        "\n",
        "### Datasets \n",
        "*   `training_data` is used to train your model\n",
        "*   `tournament_data` is used to evaluate your model\n",
        "\n",
        "### Column descriptions\n",
        "*   id: a randomized id that corresponds to a stock \n",
        "*   era: a period of time\n",
        "*   data_type: either `train`, `validation`, `test`, or `live` \n",
        "*   feature_*: abstract financial features of the stock \n",
        "*   target: abstract measure of stock performance\n",
        "\n",
        "\n"
      ]
    },
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      "source": [
        "# download the latest training dataset (takes around 30s)\n",
        "training_data = pd.read_csv(\"https://numerai-public-datasets.s3-us-west-2.amazonaws.com/latest_numerai_training_data.csv.xz\")\n",
        "training_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
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              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>n00679d1a636062f</td>\n",
              "      <td>era1</td>\n",
              "      <td>train</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.5</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 314 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id   era  ... feature_wisdom46  target\n",
              "0  n000315175b67977  era1  ...             0.75              0.75\n",
              "1  n0014af834a96cdd  era1  ...             1.00              0.25\n",
              "2  n001c93979ac41d4  era1  ...             0.75              0.00\n",
              "3  n0034e4143f22a13  era1  ...             1.00              0.00\n",
              "4  n00679d1a636062f  era1  ...             0.75              0.75\n",
              "\n",
              "[5 rows x 314 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6GkhBurt73Fi",
        "colab_type": "code",
        "outputId": "aeb5461c-5abd-41e2-92d0-3c151faf8eff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "# download the latest tournament dataset (takes around 30s)\n",
        "tournament_data = pd.read_csv(\"https://numerai-public-datasets.s3-us-west-2.amazonaws.com/latest_numerai_tournament_data.csv.xz\")\n",
        "tournament_data.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
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              "    }\n",
              "\n",
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              "    }\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>era</th>\n",
              "      <th>data_type</th>\n",
              "      <th>feature_intelligence1</th>\n",
              "      <th>feature_intelligence2</th>\n",
              "      <th>feature_intelligence3</th>\n",
              "      <th>feature_intelligence4</th>\n",
              "      <th>feature_intelligence5</th>\n",
              "      <th>feature_intelligence6</th>\n",
              "      <th>feature_intelligence7</th>\n",
              "      <th>feature_intelligence8</th>\n",
              "      <th>feature_intelligence9</th>\n",
              "      <th>feature_intelligence10</th>\n",
              "      <th>feature_intelligence11</th>\n",
              "      <th>feature_intelligence12</th>\n",
              "      <th>feature_charisma1</th>\n",
              "      <th>feature_charisma2</th>\n",
              "      <th>feature_charisma3</th>\n",
              "      <th>feature_charisma4</th>\n",
              "      <th>feature_charisma5</th>\n",
              "      <th>feature_charisma6</th>\n",
              "      <th>feature_charisma7</th>\n",
              "      <th>feature_charisma8</th>\n",
              "      <th>feature_charisma9</th>\n",
              "      <th>feature_charisma10</th>\n",
              "      <th>feature_charisma11</th>\n",
              "      <th>feature_charisma12</th>\n",
              "      <th>feature_charisma13</th>\n",
              "      <th>feature_charisma14</th>\n",
              "      <th>feature_charisma15</th>\n",
              "      <th>feature_charisma16</th>\n",
              "      <th>feature_charisma17</th>\n",
              "      <th>feature_charisma18</th>\n",
              "      <th>feature_charisma19</th>\n",
              "      <th>feature_charisma20</th>\n",
              "      <th>feature_charisma21</th>\n",
              "      <th>feature_charisma22</th>\n",
              "      <th>feature_charisma23</th>\n",
              "      <th>feature_charisma24</th>\n",
              "      <th>feature_charisma25</th>\n",
              "      <th>...</th>\n",
              "      <th>feature_wisdom8</th>\n",
              "      <th>feature_wisdom9</th>\n",
              "      <th>feature_wisdom10</th>\n",
              "      <th>feature_wisdom11</th>\n",
              "      <th>feature_wisdom12</th>\n",
              "      <th>feature_wisdom13</th>\n",
              "      <th>feature_wisdom14</th>\n",
              "      <th>feature_wisdom15</th>\n",
              "      <th>feature_wisdom16</th>\n",
              "      <th>feature_wisdom17</th>\n",
              "      <th>feature_wisdom18</th>\n",
              "      <th>feature_wisdom19</th>\n",
              "      <th>feature_wisdom20</th>\n",
              "      <th>feature_wisdom21</th>\n",
              "      <th>feature_wisdom22</th>\n",
              "      <th>feature_wisdom23</th>\n",
              "      <th>feature_wisdom24</th>\n",
              "      <th>feature_wisdom25</th>\n",
              "      <th>feature_wisdom26</th>\n",
              "      <th>feature_wisdom27</th>\n",
              "      <th>feature_wisdom28</th>\n",
              "      <th>feature_wisdom29</th>\n",
              "      <th>feature_wisdom30</th>\n",
              "      <th>feature_wisdom31</th>\n",
              "      <th>feature_wisdom32</th>\n",
              "      <th>feature_wisdom33</th>\n",
              "      <th>feature_wisdom34</th>\n",
              "      <th>feature_wisdom35</th>\n",
              "      <th>feature_wisdom36</th>\n",
              "      <th>feature_wisdom37</th>\n",
              "      <th>feature_wisdom38</th>\n",
              "      <th>feature_wisdom39</th>\n",
              "      <th>feature_wisdom40</th>\n",
              "      <th>feature_wisdom41</th>\n",
              "      <th>feature_wisdom42</th>\n",
              "      <th>feature_wisdom43</th>\n",
              "      <th>feature_wisdom44</th>\n",
              "      <th>feature_wisdom45</th>\n",
              "      <th>feature_wisdom46</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>n000920ed083903f</td>\n",
              "      <td>era121</td>\n",
              "      <td>validation</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>1.00</td>\n",
              "      <td>0.25</td>\n",
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              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>n0038e640522c4a6</td>\n",
              "      <td>era121</td>\n",
              "      <td>validation</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>n004ac94a87dc54b</td>\n",
              "      <td>era121</td>\n",
              "      <td>validation</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>n0052fe97ea0c05f</td>\n",
              "      <td>era121</td>\n",
              "      <td>validation</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 314 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id     era  ... feature_wisdom46  target\n",
              "0  n0003aa52cab36c2  era121  ...             0.00              0.00\n",
              "1  n000920ed083903f  era121  ...             0.50              0.25\n",
              "2  n0038e640522c4a6  era121  ...             0.00              1.00\n",
              "3  n004ac94a87dc54b  era121  ...             0.25              0.75\n",
              "4  n0052fe97ea0c05f  era121  ...             1.00              1.00\n",
              "\n",
              "[5 rows x 314 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c3Fcz-PpHYez",
        "colab_type": "text"
      },
      "source": [
        "## 3. Train your first model\n",
        "Let's create a basic model using sklearn's linear regression."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ESazJMMzN--g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# find only the feature columns\n",
        "feature_cols = training_data.columns[training_data.columns.str.startswith('feature')]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Bmbqi4YKY2Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# select those columns out of the training dataset\n",
        "training_features = training_data[feature_cols]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AuNXbVmRQDp",
        "colab_type": "code",
        "outputId": "d88d95fd-09f7-47ac-9ee7-3e0bacd47cae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# create a model and fit the training data (~30 sec to run)\n",
        "model = sklearn.linear_model.LinearRegression()\n",
        "model.fit(training_features, training_data.target)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uxx5pLIGIUvN",
        "colab_type": "text"
      },
      "source": [
        "## 4. Generate your first predictions\n",
        "Now that we have a trained model, we can use it to make predictions on the tournament data.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIcmOiJYSmJI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# select the feature columns from the tournament data\n",
        "live_features = tournament_data[feature_cols]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t4CoaCgbKcSN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# predict the target on the live features\n",
        "predictions = model.predict(live_features)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4L4BFeAzTTDe",
        "colab_type": "code",
        "outputId": "692b78f5-20b1-4431-8d53-ceef876f8101",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# predictions must have an `id` column and a `prediction` column\n",
        "predictions_df = tournament_data[\"id\"].to_frame()\n",
        "predictions_df[\"prediction\"] = predictions\n",
        "predictions_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>n0003aa52cab36c2</td>\n",
              "      <td>0.472981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>n000920ed083903f</td>\n",
              "      <td>0.492854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>n0038e640522c4a6</td>\n",
              "      <td>0.556868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>n004ac94a87dc54b</td>\n",
              "      <td>0.496384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>n0052fe97ea0c05f</td>\n",
              "      <td>0.497034</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id  prediction\n",
              "0  n0003aa52cab36c2              0.472981\n",
              "1  n000920ed083903f              0.492854\n",
              "2  n0038e640522c4a6              0.556868\n",
              "3  n004ac94a87dc54b              0.496384\n",
              "4  n0052fe97ea0c05f              0.497034"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQ0sgtPiLDys",
        "colab_type": "text"
      },
      "source": [
        "## 5. Make your first submission\n",
        "To enter the tournament, we must submit the predictions back to Numerai. We will use the `numerapi` library to do this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEfqpxcEWDdK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Get your API keys and model_id from https://numer.ai/notebook\n",
        "public_id = \"REPLACEME\"\n",
        "secret_key = \"REPLACEME\"\n",
        "model_id = \"REPLACEME\"\n",
        "napi = numerapi.NumerAPI(public_id=public_id, secret_key=secret_key)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WeAIJHaoW3VU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Upload your predictions\n",
        "predictions_df.to_csv(\"predictions.csv\", index=False)\n",
        "submission_id = napi.upload_predictions(\"predictions.csv\", model_id=model_id)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Jc2NgEgQEKF",
        "colab_type": "text"
      },
      "source": [
        "# Done 🚀\n",
        "Good job! You just made your first submission on Numerai!\n",
        "\n",
        "Head back over to https://numer.ai/notebook to continue."
      ]
    }
  ]
}