Machine Learning Prototyping with DuckDB and scikit-learn

This post explores how DuckDB, an efficient data management system, complements scikit-learn, a popular machine learning library, in developing a species prediction model using the Palmer Penguins dataset. Key steps include data preprocessing with DuckDB, model training using a Random Forest classifier, and three inference methods to achieve predictions: using Pandas, DuckDB UDF row by row, and DuckDB batch style. Performance implications of UDFs are discussed, highlighting their utility despite slower execution times compared to Pandas.