For 2025-07, the developer delivered the Customer Churn Analysis Notebook in giovanirojascuela/2025_5TO01, enabling data-driven churn insights for business decisions. The notebook covers data preprocessing, logistic regression modeling, evaluation, and visualizations of tenure, monthly charges, contract type, and churn to support retention strategies and pricing discussions. This work enhances the ability to identify at-risk customers, quantify churn drivers, and inform strategic decisions with reproducible pipelines. No major bugs were reported this month; the deliverable emphasizes reproducibility, clear documentation, and business-value findings.

June 2025 monthly summary for Giovanirojascuela/2025_5TO01: Delivered foundational data analytics and ML prep work for the coffee sales initiative and improved repository hygiene. Key results include Colab-based notebooks for data loading, combining datasets, exploratory data analysis, and visuals, plus an ML-ready scaffold. Completed cleanup of outdated notebooks to reduce confusion and maintenance overhead. Impact: faster experimentation, clearer data workflow, and improved collaboration readiness. Technologies: Google Colab, Python data stack (Pandas, matplotlib/seaborn), basic ML prep workflows, version control hygiene.

May 2025 focused on delivering features, establishing API scaffolding, and enhancing documentation across two repositories. Key REST API work includes a new Express.js CRUD API for a Libro resource backed by a JSON file, and a Country Data API scaffold with CRUD routes and dev tooling. JavaScript fundamentals and educational demos were introduced to aid learning and onboarding. Documentation improvements consolidated contributor lists, added Markdown usage examples, and expanded educational materials to accelerate onboarding and knowledge sharing.