Football Virtual betting Model

Predicted the outcome of football matches by analyzing team statistics, player performance and historical data

Created an ensemble model using XGBoost, Deep Neural Network, and LSTM

Designed and implemented data preprocessing techniques to clean and format data collected from multiple sources, such as team statistics and player performance records.

The model was able to accurately predict the outcome of football matches with a high degree of accuracy, resulting in improved betting results for clients.

The model was integrated into a betting platform and made available to clients, leading to an increase in user engagement and satisfaction.

The project received positive feedback from clients, who noted the model's ability to provide reliable and accurate predictions.