mvp-horse-racing-prediction

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mvp-horse-racing-prediction

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Using machine learning to predict HK Horse racing results

// repository documentation

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# Hong Kong Horse Racing Prediction

The aim of this project is to predict the outcome of horse racing using machine learning algorithms.

![horse_racing](https://user-images.githubusercontent.com/71772293/123072394-ca65c100-d415-11eb-9660-24d726f7541d.jpeg)

From RaceBets

## Dataset

The dataset comes from [Kaggle](https://www.kaggle.com/gdaley/hkracing) and covers races in HK from **1997 to 2005**. <br>
The data consists of **6,349** races with 4,405 runners. <br>
The 5,878 races ran before January 2005 are used to develop the forecasting models whereas the remaining 471 races ran after January 2005 are preserved to conduct out-of-sample testing.

We have an article explaining our journey through this process. You can find a link below:
* **[Horse Racing Prediction Part 1](https://medium.com/codeworksparis/horse-racing-prediction-a-machine-learning-approach-part-1-44ed7fca869e)**
* **[Horse Racing Prediction Part 2](https://medium.com/codeworksparis/horse-racing-prediction-a-machine-learning-approach-part-2-e9f5eb9a92e9)**

## Documentation

* `requirements.txt`: list of requirements needed to run this project
* `baseline_models.ipynb`: notebook containing informations for part 1 on baseline models
* `quick_eda_horse_racing.ipynb`: notebook with a quick EDA on our dataset
* `create_dataset.py` and `config.py` are both used to split our inital data into train and test sets depending on the date of races
* `extract_features.py` is used to perform feature engineering
* `winner/`: folder containing all notebooks and ML models to bet on the winner
* `placed/`: folder containing all notebooks and ML models to bet on placed horses (the Top 3)

### winner folder 🏆

Let's have a look about the winner files

* `winner_01_lgbm_optim`: runs the hyperoptimization for LGBM
* `winner_02_train`: runs all training processes either for LGBM and deep learning then saves results
* `winner_03_show_result`: helps us to verify our informations and go deeper about our predictions for a specific month
* `winner_04_all_results`: consolidates all months with an ensemble model and shows final results
* `winner_functions.py`: contains the required functions to run those 4 previous notebooks
* `model/`: contains all saved models from winner_02_train
* `result_hyperopt.csv`: file with all our optimizations steps

### placed folder 🥇🥈🥉

Let's have a look about the placed files

* `placed_01_train`: runs all training processes for deep learning then saves results
* `placed_02_show_result`: helps us to verify our informations and go deeper about our predictions for a specific month
* `placed_03_consolidated`: consolidates all months with an ensemble model and shows final results
* `placed_functions.py`: contains the required functions to run those 4 previous notebooks
* `model/`: contains all saved models from placed_01_train and LGBM models from winner_folder

mvp-horse-racing-prediction

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