🎯 Swimming Coach | Aspiring Data Scientist & AI Engineer | Passionate About Tech & Human Performance
📍 Coimbra, Portugal 📫 Email 💼 LinkedIn

Graduated in Sports Science with a postgraduate degree in High Performance Training, I began my professional career in the field of sports (Swimming Coach).

At the same time, I have been actively building a second path in an area that has always fascinated me: technology. I discovered a strong interest in programming. This curiosity led me to independently explore key areas of Data Science and Artificial Intelligence, including Python, Statistics and Probability, Data Manipulation and Analysis, Machine Learning, Deep Learning, Data Visualization, and Data Ethics and Privacy.

Languages & Libraries

• Python (Pandas, Scikit-Learn, TensorFlow, Matplotlib, Numpy, XGBoost)
• SQL

Machine Learning

• Data Engineering
• Supervised & Unsupervised Learning
• Deep Learning

Natural Language Processing

• Tokenization
• Vectorization
• Text Classification
• Model Evaluation

Tools

• Linux
• Git
• Jupyter Notebooks
• Docker

Image Classifier API

• A Dockerized FastAPI application for classifying flower images with a custom CNN trained on the TensorFlow Flowers dataset. It provides a training pipeline and an endpoint that returns predicted classes with confidence scores.

Historical AQI Prediction in EUA

• A project that uses deep learning to forecast U.S. Air Quality Index (AQI) values for NO₂, O₃, SO₂, and CO based on official EPA data from 2000–2016. It combines data preprocessing and feature engineering with a Keras model and provides FastAPI and Streamlit components for serving and visualizing predictions

Olympic Performance Swimming

• The Olympic Performance Swimming repo implements a small machine learning pipeline to predict whether Olympic swimmers win medals. It uses data preparation, an XGBoost model tuned with Optuna, and generates visualizations to analyze over 23,000 swimming performances