MLB_Runline_Training.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gTKL-2tRlNIT"
      },
      "outputs": [],
      "source": [
        "!pip install pycaret\n",
        "!pip install mysql-connector-python\n",
        "!pip install sqlalchemy==1.4.32"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "W1oH3E8Nln9t"
      },
      "outputs": [],
      "source": [
        "import pycaret\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sqlalchemy\n",
        "import mysql.connector\n",
        "\n",
        "from pycaret import classification\n",
        "from google.colab import drive\n",
        "from datetime import datetime\n",
        "from google.colab.data_table import DataTable\n",
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dNNe7YwMlpgP"
      },
      "outputs": [],
      "source": [
        "engine = sqlalchemy.create_engine('mysql+mysqlconnector://username:password@database-host-name:3306/database-name')\n",
        "\n",
        "Raw_Dataset = pd.read_sql(sql = \"SELECT * FROM baseball_spread\", con = engine).set_index(\"game_datetime\").reset_index()[[\"game_datetime\", \"team_1\",\t\"team_1_spread_odds\",\t\"team_2\",\t\"team_2_spread_odds\",\t\"venue_name\",\t\"spread\"]]\n",
        "print(len(Raw_Dataset))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K4RnhrRmKlwR"
      },
      "outputs": [],
      "source": [
        "Raw_Dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Classification_Setup = pycaret.classification.setup(data = Raw_Dataset, target = \"spread\", train_size = .70, use_gpu = False, data_split_stratify = False, data_split_shuffle = False, date_features = [\"game_datetime\"], categorical_features = [\"team_1\",\"team_2\",\"venue_name\"])"
      ],
      "metadata": {
        "id": "Wb0v2bKsmJOc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnTnG8aSK74e"
      },
      "source": [
        "***Compare/Create Model***"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Classification_Models = Classification_Setup.compare_models(turbo = False)"
      ],
      "metadata": {
        "id": "YTAPYfpanoXE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can use the multi-layer perceptron (\"mlp\") as outlined in the post, but a random forest (\"rf\") classification is more effective. A neural-network is a ***bit*** of an overkill."
      ],
      "metadata": {
        "id": "KK66V4NXFy0n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Classification_Model = Classification_Setup.create_model(\"rf\")"
      ],
      "metadata": {
        "id": "tY82ShLirM3B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Prediction = Classification_Setup.predict_model(Classification_Model)\n",
        "Prediction.head(20)"
      ],
      "metadata": {
        "id": "ZKYRxxPLXsE3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Classification_Setup.evaluate_model(Classification_Model)"
      ],
      "metadata": {
        "id": "Cjj3McLgxFfT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "odO3tScVvMTy"
      },
      "source": [
        "***Save Model***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d16cozQ4vLxv"
      },
      "outputs": [],
      "source": [
        "Finalized_Model = pycaret.classification.finalize_model(Classification_Model)\n",
        "Finalized_Model_save_to_file_string = f\"{datetime.today().strftime('%Y-%m-%d')} Baseball Spread\"\n",
        "pycaret.classification.save_model(Finalized_Model, Finalized_Model_save_to_file_string)\n",
        "files.download(f\"{Finalized_Model_save_to_file_string}.pkl\")"
      ]
    }
  ]
}