A machine learning (supervised learning) model trained on past auction data from multiple auction houses, capable of estimating the value a whisky cask.
Applied tools: Python, R, Scikit-learn and Streamlit.
Applied skills: data analysis, ETL, machine learning and web scraping.
A recommendation system that, based on the user’s tasting profile, recommends the most similar bottle based on its tasting notes.
Applied tools: Python, R and Streamlit.
Applied skills: data visualization, ETL, natural language processing, recommendation systems and web scraping.
Dashboard developed to analyze the average prices of six types of fuel across Brazil over time and at different territorial levels, including regions, states, and cities
Applied tools: Plotly, PySpark, Python and Streamlit.
Applied skills: big data, data analysis, data visualization and ETL.
Dashboard created to support decision-making for real estate purchases in the city of Maringá, Brazil. The project also includes ETL pipelines built from three real estate agency websites.
Applied tools: Power BI and Python.
Applied skills: data analysis, data visualization, ETL and web scraping.
ETL process developed to extract, transform and load data from whisky auction houses intended to feed my personal data analysis and machine learning projects related to whisky casks.
Applied tools: Python and R.
Applied skills: ETL and web scraping.
Composed by 4 routes:
Applied tools: FastAPI and Python.
Applied skills: economics, finance, python development.