Bibliografía

• Nasir, J., Shahmoradi, S., Wang, C., Bruno, B., & Dillenbourg, P. (2025). Jupyter Analytics: A Toolkit for Collecting, Analyzing, and Visualizing Learning Activities in Jupyter. En Proceedings of the 2025 ACM Conference on Learning at Scale (pp. 1–10)._ Association for Computing Machinery. https://doi.org/10.1145/3641554.3701971
• Kamlofsky, J., Manzano, F., & López Yse, D. (2024). Tareas ETL más simples con Pandas: Funciones útiles aplicadas sobre datos públicos. Revista Abierta de Informática Aplicada, 8(1), 35–58. https://doi.org/10.59471/raia2024204
• Sahoo, S. K., & Mohapatra, D. P. (2016). A comparative study of regression model and machine learning techniques in software effort estimation. 2016 International Conference on Computing, Communication and Automation (ICCCA), 1–7. https://doi.org/10.1109/CCAA.2016.7813816
• Tosi, L. (2013). Learning matplotlib. Packt Publishing Ltd. Recuperado de https://books.google.com.ec/books?id=8Fs3AgAAQBAJ
• Bokeh Development Team. (2024). Bokeh: Python interactive visualization library. https://docs.bokeh.org/en/latest/
• Yu, Y., Shen, L., Long, F., Qu, H., & Chen, H. (2024). PyGWalker: Asistente sobre la marcha para el análisis exploratorio de datos visuales. 2024 IEEE Visualization and Visual Analytics (VIS), 6–10. https://doi.org/10.1109/VIS55277.2024.00009