This repository contains Python code snippets for various football-related tasks, including data scraping, data manipulation, and model training.
test.py trains and tests a machine learning model based on scraped football data from fbref.com. The model predicts the outcomes of football matches (Win, Draw, or Lose) based on the teams playing.
- Ensure you have the required libraries installed (e.g., PyTorch, pandas).
- Run the code using
python test.py. - The code will preprocess the data, train a neural network model, and evaluate its performance.
fbref_scrape.py scrapes Premier League fixtures and results data from fbref.com and saves it in a CSV file. It includes season-specific data retrieval and data parsing.
- Install the required libraries (requests, BeautifulSoup).
- Run the script using
python scraping/fbref_scrape.py. - The data will be scraped and saved in a CSV file named
data/fixture_results.csv.
clean_data.py reads a CSV file containing Premier League fixture results, cleans the data, and saves the cleaned data to a new CSV file. It includes column renaming, special character replacement, date formatting, and match result determination.
- Ensure you have pandas installed.
- Run the code using
python data/clean_data.py. - The script will read, clean, and save the data as
cleaned_fixture_results.csv.
The code snippets have specific library requirements, which are mentioned in each snippet's description. You can install the required libraries using pip install <library-name> or by running pip install -r requirements.txt
