This project aims to predict the value of football teams using regression techniques. The value of a football team is a crucial metric for various stakeholders including club owners, investors, and fans. By predicting the value of a team, we can gain insights into its financial standing and potential performance.
The dataset used for this project consists of historical data on football teams, including various attributes such as player salaries, team performance metrics, market value, and other relevant features. The dataset is sourced from reputable sources and has been preprocessed to ensure data quality.
We employ regression techniques to predict the value of football teams based on the available features. Various regression algorithms such as linear regression, polynomial regression, and ensemble methods are explored and compared to determine the most accurate model.