Serie A Predictor ⚽

This repository provides a machine learning and data visualization project focused on predicting the Serie A league table for the 2025-2026 season using historical match data.
It combines data preprocessing, feature engineering, and predictive modeling with visual exploratory analysis.

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📂 Project Structure

• Serie_A_predictor.py
  Main Python script that:

  • Loads the dataset matches_serie_A.csv
  • Cleans and preprocesses the data
  • Generates additional season-based CSV datasets saved in the Season_data/ folder
  • Trains a Random Forest model to predict the final league standings for the 2025-2026 season

• Serie_A_visualizations_notebook.ipynb
  Jupyter Notebook used for data visualization and analysis.
  It includes:

  • Exploratory plots created with Matplotlib and Seaborn
  • Commentary and insights for each visualization
  • Exported plots saved in the Plot/ folder

• matches_serie_A.csv
  Input dataset containing historical Serie A match results used for training and analysis.

• Season_data/
  Auto-generated folder containing season-specific processed datasets.

• Plot/
  Folder containing exported data visualizations.

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🔧 Installation & Requirements

Make sure you have Python 3.8+ installed. Then, install the required dependencies:

pip install -r requirements.txt

Typical requirements include:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• jupyter

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🚀 Usage

1. Run the predictor script

   python Serie_A_predictor.py

   This will clean the dataset, generate season-based CSV files, and predict the Serie A table for 2025-2026.

2. Explore data visualizations
   Open the Jupyter Notebook:

   Serie_A_visualizations_notebook.ipynb

   The generated plots are also saved inside the Plot/ folder.

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📊 Model

The predictive model is based on a Random Forest Classifier trained on past Serie A seasons.
The pipeline includes:

• Data cleaning & preprocessing
• Feature engineering (team statistics, match results, season summaries)
• Model training & prediction of league standings

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🤝 Contributing

Contributions, issues, and feature requests are welcome!
Feel free to open a pull request to improve the model, dataset, or visualizations.

P.S thx to @Erik-Cupsa for the inspiration

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📜 License

This project is released under the MIT License.