Documenting code, one commit at a time.
Working on the data-science-portfolio project involves creating a centralized space to showcase analytical capabilities. A key challenge in maintaining a portfolio is keeping data exploration, modeling pipelines, and visual outputs organized and reproducible for stakeholders.
Initially, data projects were scattered across
Building a robust fraud detection system requires balancing real-time data ingestion with complex analytical processing. In the project pipeline-deteccion-fraudes, we have recently implemented the first version of a modular pipeline designed to handle incoming transaction streams and apply intelligent filtering logic.
At the core of this system is the
Building a robust data science portfolio is more than just stacking models; it's about creating a narrative that others can follow. Recently, I have been updating the 'data-science-portfolio' project to better organize analysis workflows and improve the transparency of my research pipelines.
In data science, we often fall into the trap of monolithic Jupyter
The data-science-portfolio project is a collection of analytical workflows and machine learning experiments. As these projects grow in complexity, moving from scattered scripts to a structured environment becomes essential for maintaining reproducibility and ease of collaboration.
When working with libraries like Scikit-learn,
Maintaining a data science portfolio is more than just stacking Jupyter notebooks. As we explore new modeling techniques and datasets, our repositories can quickly become cluttered with outdated experiments or obsolete research code. Recently, I performed a audit of my data-science-portfolio to streamline its structure.
Over time, I accumulated
