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Football Predictions and Web Scraping Project

This project aims to build and use a football match prediction model by gathering and analyzing data on teams, players, and match results. It consists of two key components: data gathering through web scraping and predictive modeling for match outcomes.

Table of Contents

Project Overview

This project is divided into two main parts:

  1. Data Collection: Gathering football-related data from various websites using web scraping.
  2. Prediction Model: Developing a machine learning model to predict match results based on historical data.

The project uses Jupyter notebooks for code organization and documentation, with Python libraries like requests, BeautifulSoup, and scikit-learn.

Installation

  1. Clone this repository:

    git clone https://github.com/yourusername/Football-Predictions.git
cd Football-Predictions

  2. Install the required dependencies:

    pip install -r requirements.txt

  3. Open the Jupyter notebooks:

    jupyter notebook

Data Collection

The web scraping component, located in the 4_Webscrapping.ipynb notebook, is responsible for gathering football data from various sources. It focuses on fetching the following information:

  • Team statistics
  • Player information
  • Match outcomes and scores

Key Details:

  • Scraping Libraries: Uses requests to handle HTTP requests and BeautifulSoup to parse HTML.
  • Data Cleaning: Once scraped, the data undergoes preliminary cleaning and structuring to make it suitable for analysis.

To start the data scraping process:

  1. Run each cell sequentially in the 4_Webscrapping.ipynb notebook.
  2. Scraped data will be saved in the specified output files, ready to be used in the prediction model.

Note: Be mindful of the website’s terms of service when scraping data. Use delays and limit requests to avoid potential bans.

Football Prediction Model

The prediction model, developed in the 4_Football Predictions.ipynb and Football Predictions.ipynb notebooks, uses the scraped data to predict the outcomes of future football matches.

Steps:

  1. Data Preparation: Data is preprocessed to remove inconsistencies and null values, and features are engineered to enhance model performance.
  2. Feature Selection: Important features are selected based on correlation and significance tests.
  3. Model Building: Several machine learning models, such as Logistic Regression, Random Forest, and SVM, are evaluated.
  4. Performance Evaluation: Models are assessed using accuracy, precision, recall, and F1-score metrics to identify the best-performing model.

The output of the model is a prediction on match results (e.g., Win, Lose, or Draw), which can assist in decision-making or serve as a tool for sports enthusiasts.

Usage

  1. Web Scraping: Open 4_Webscrapping.ipynb and run the cells to scrape and save data.
  2. Prediction Model:
    • Open 4_Football Predictions.ipynb or Football Predictions.ipynb.
    • Ensure the required data from the scraping step is available.
    • Execute the cells to train and evaluate the model.

Example of running the prediction pipeline:

# Assuming the data has already been collected and preprocessed

# Train the model
train_model(data)

# Predict upcoming matches
predictions = predict_matches(upcoming_data)

Dependencies

The project requires the following Python libraries:

  • requests
  • BeautifulSoup4
  • pandas
  • scikit-learn
  • matplotlib
  • seaborn
  • jupyter

Ensure all dependencies are installed by running:

pip install -r requirements.txt

License

This project is licensed under the MIT License. See the LICENSE file for details.