🎓 Master's Student in Data Science at University of Naples Federico II
📊 Passionate about Data Science, Machine Learning, and Artificial Intelligence
🤖 Interested in Natural Language Processing, Predictive Modeling, and Generative AI
🚀 Building real-world data science and AI projects
🌍 Open to opportunities as:
• Data Analyst
• Junior Data Scientist
• Machine Learning Engineer
• AI Engineer
Machine Learning • Deep Learning • Statistical Analysis • Predictive Modeling • Feature Engineering • Model Evaluation
Pandas • NumPy • Matplotlib • Seaborn • Plotly • Power BI
Natural Language Processing (NLP) • Large Language Models (LLMs) • Retrieval-Augmented Generation (RAG) • LangChain • Text Mining
Git • GitHub • Jupyter Notebook • Google Colab • Streamlit • SQLite
• Deep Learning with PyTorch
• Large Language Model (LLM) Applications
• Advanced Machine Learning
• MLOps Fundamentals
Built a conversational AI application that enables users to upload PDF documents and interact with them using Large Language Models with Retrieval-Augmented Generation (RAG).
Technologies:
Python • LangChain • Streamlit • HuggingFace Transformers • NLP • LLMs • RAG • Document Processing
End-to-end data science project analyzing SpaceX launch data to identify factors influencing rocket landing success and build predictive machine learning models.
Technologies:
Python • Pandas • NumPy • SQL • Scikit-learn • Matplotlib • Seaborn • Plotly Dash • Folium • Machine Learning • Data Analysis
Machine learning and deep learning models for classifying news articles as real or fake using NLP techniques and advanced text representations.
Technologies:
Python • NLP • TF-IDF • Word2Vec • Scikit-learn • Deep Learning • LSTM • GRU • Transformers • Text Classification
Forecasting insect colony weight using environmental sensor data with time series modeling and interactive data visualization dashboards.
Technologies:
Python • Time Series Analysis • ARIMA • SARIMA • Pandas • Data Visualization • Power BI • Streamlit
Machine learning regression models developed to predict housing prices using the California Census dataset with feature engineering and hyperparameter tuning.
Technologies:
Python • Pandas • NumPy • Scikit-learn • Random Forest • Regression Models • Grid Search • Data Preprocessing
Master's in Data Science
University of Naples Federico II
Apple Developer Academy
University of Naples Federico II