This is a data science project that involves scraping data from fbref.com, analyzing and cleaning the data, and then using a Random Forest algorithm to predict if a football team wins or not. The project is implemented in Python and uses several libraries, including Pandas, Beautiful Soup, and Scikit-Learn.
Scraping data from a website involves programmatically accessing the website's HTML and CSS code and extracting the relevant data. This data can be structured in a tabular form using a library like Pandas. Once the data is in a Pandas dataframe, it can be analyzed, cleaned, and pre-processed for machine learning purposes.
Random Forest is a type of ensemble learning algorithm that can be used for classification or regression tasks. It works by building multiple decision trees and then combining their predictions to make a final prediction. In this case, the goal is to predict whether a team wins or not based on various features such as the number of shots, venue, opposition, time etc.