Horse Race Results Prediction Using Machine Learning Algorithms With Feature Selection
Keywords:
Machine Learning, Random forest, Horse race results prediction, feature selectionAbstract
People's interest in horse racing has skyrocketed along with its rapid expansion. Some experts and academics have studied the best practices for managing decisions and making predictions in horse racing. In the areas of categorization and prediction, applying the machine learning (ML) paradigms has demonstrated hopeful results. Betting on sports is big business, making accurate predictions in this field increasingly important. In addition, club executives are looking for classification models to better comprehend the game and develop winning plans. Research has shown that Machine Learning algorithms offer a good answer to the categorization and prediction problem in horse racing, where traditional prediction algorithms have failed. In this study, we present several ML approaches for predicting the outcome of horse races, including K-nearest-neighbor (KNN), Linear-regression (LR), Randomforest(RF), Gaussian NaiveBayes (NB), ADA Boost (BAG), along with Bagging. These models take into account several aspects of the games, including past match outcomes, player and horse statistics, information about the competition, and more. The results of the massive-scale studies showed that the RF approach produced more accurate predictions than any of the other models tested. We believe that researchers who delve into this field in the future will find our work both enlightening and useful.
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