I'm an AI/ML engineer with over five years of programming experience in Python. I've tackled data science projects that allowed me to apply my knowledge in many different business settings, delivering important insights through the development of machine learning models and data visualization pipelines. I'm currently working on the intersection between ML and Cybersecurity. Most of my work is available on GitHub.
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This project focuses on forecasting weekly retail sales using the Walmart Store Sales dataset. The main objective is to predict future sales from historical store and department level data, with an emphasis on improving WMAE performance while keeping experiments reproducible and easy to compare through MLflow-tracked models, convergence plots, and feature engineering iterations.
A local security monitoring platform built in Python, React, and PostgreSQL. The system continuously collects process and network telemetry from the host machine, stores it locally, and trains unsupervised machine learning models to learn what normal behavior looks like. When something deviates from that baseline, an alert is generated, analyzed by a local LLM running through Ollama, and surfaced in a real-time dashboard.
This project uses an LLM-based multidimensional sentiment analysis of user reviews to explore how audiences respond to games across genres and styles. Using official Steam reviews, it assesses not just positive or negative reactions, but also how players express enjoyment, frustration, immersion, value, and technical satisfaction.
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