🎓 Computer Science Student at Universidade Federal Fluminense (UFF) 📍 7th semester | Focused on Data Science, Machine Learning & Data Engineering
I'm a Computer Science student passionate about transforming data into decisions.
My main interests are:
- ⚽ Sports Analytics (Football Data)
- 🤖 Machine Learning & AI
- 🧱 Data Engineering & Pipelines
- 📊 Clustering, Similarity Search & Recommender Systems
I enjoy building end-to-end projects — from data collection and ETL to modeling, evaluation, and visualization.
- Python (Pandas, NumPy)
- SQL
- Web Scraping (BeautifulSoup, Requests)
- scikit-learn
- Clustering (K-Means, Silhouette Score)
- Regression & Classification
- Feature Engineering & Scaling
- Cosine Similarity
- ETL Pipelines
- Data Lakes
- Structured Datasets
- Power BI
- Matplotlib / Seaborn
An AI-driven scouting system that:
- Transforms football statistics into vector representations
- Clusters players by playstyle
- Represents teams as aggregated player vectors
- Uses cosine similarity to rank player–team compatibility
🔍 Result: the model independently highlighted Jude Bellingham as a top match for Real Madrid using 2022–23 data — aligned with the real-world transfer.
Technologies:
- Python, Pandas, scikit-learn
- K-Means, PCA, Cosine Similarity
- ETL + Data Lake architecture
- Advanced ML evaluation techniques
- Model interpretability
- Better feature selection for unsupervised learning
📫 Feel free to explore my repositories and reach out — I'm always open to discussing data, football, and AI!