Comparative Analysis of Machine Learning Algorithms
for Predicting Horse Racing Outcomes

Background

Horse racing, a widely favoured form of gambling, enjoys substantial popularity in various regions worldwide, including Hong Kong, Japan, and Great Britain. Prior to each race, a “racing booklet” is published, containing essential information about the forthcoming event. This booklet includes details about the participating horses, jockeys, the track conditions, and historical race outcomes, all crucial for formulating predictions about race results and guiding more informed betting decisions. The informed and data-rich nature of horse racing favours the use of scientific approaches for systematic prediction. In academia, horse racing serves as a testbed for machine learning scientists to present and compare the latest machine learning algorithms.
 
In this research, several machine learning techniques will be employed to develop different machine learning models that predicts horse racing outcomes. The objective of this project is to construct a model that can accurately predict these outcomes, thereby assisting in making informed betting choices. Additionally, the study will investigate the factors that significantly influence race outcomes and evaluate the performance of the model when various deep machine algorithms are implemented.

The structure of this project will be composed as follows. In stage 1, a single learning model will be trained. This stage aims at constructing a dataset, which involves data collection and cleaning work. The outcome of this stage will be used to support stage 2 study, which is focus on a comparative analysis of the performance of a selected set of machine learning algorithm.

Objective

1. Understand the historic racing data
For every machine learning project, it is essential to delve in to the data, recognizing fields which are significant to the prediction . This understanding will help in refining the data, eliminating irrelevant or noisy information, and setting a solid foundation for predictive accuracy.
 
2. Optimization of the machine learning model
To improve the performance of the machine learning model, extensive work on optimization, such as fine-tuning and weight initialization, has to be carried out. Additionally, the use of cloud computing can be considered to meet the requirements for computational resources during training. Cloud platforms offer advantages in terms of scalability and often provide GPUs specialized for model training, which can reduce training times and offer a cost-effective solution for project management.
 
3. Explore the performance of different machine learning algorithm
The second stage of the project entails a systematic comparative analysis of various machine learning algorithms. A select group of algorithms will be studied to determine which performs best under different conditions relevant to horse racing competitions. Factors like model complexity and computational resource demands will be considered. This comparative analysis will not only identify the model with the best overall performance but also provide deep insights into each model’s strengths and weaknesses. The outcomes of this study will enhance our understanding of how to optimize the use of different machine algorithms for prediction tasks, applicable not only to horse racing but also to daily practical scenarios.