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

From RaceBets

The dataset comes from Kaggle and covers races in HK from 1997 to 2005.
The data consists of 6,349 races with 4,405 runners.
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.

The aim of this folder is to show all notebooks and algorithms we used to predict either winner and placed horses.

We have an article explaining our journey through this process. You can find a link below:

• Part 1: https://medium.com/codeworksparis/horse-racing-prediction-a-machine-learning-approach-part-1-44ed7fca869e
• Part 2: https://medium.com/codeworksparis/horse-racing-prediction-a-machine-learning-approach-part-2-e9f5eb9a92e9

• requirement is a list of requirements needed to run this project
• baseline_models is a notebook containing informations for part 1 on baseline models
• quick_eda_horse_racing is a notebook with a quick EDA on our dataset
• create_dataset and config are both used to split our inital data into train and test sets depending on the date of races
• extract_features is used to perform feature engineering
• data is a folder containing all data once they are split by the create_dataset script
• winner is a folder containing all notebook and ML models to bet on winner horses
• placed is a folder containing all notebook and ML models to bet on placed horses

Let's have a look about the winner files:

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

Let's have a look about the placed files:

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