Provides Python-based extensions to enhance data analytics workflows, particularly for tasks involving data preprocessing and predictive modeling. Includes tools for data sampling, transformation, feature selection, balancing strategies (e.g., SMOTE), and model construction. These capabilities leverage Python libraries via the reticulate interface, enabling seamless integration with a broader machine learning ecosystem. Supports instance selection and hybrid workflows that combine R and Python functionalities for flexible and reproducible analytical pipelines. The architecture is inspired by the Experiment Lines approach, which promotes modularity, extensibility, and interoperability across tools. More information on Experiment Lines is available in Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.
Available at CRAN: https://cran.r-project.org/package=daltoolboxdp
Code repository at Git-Hub: https://github.com/cefet-rj-dal/daltoolboxdp
