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
• Installation
• Data Collection
• Football Prediction Model
• Usage
• Dependencies
• License

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.