During four months on the ICEI-PUC-Minas-PPL-CDIA/ppl-cd-pcd-sist-int-2025-1-grupo2-disparidade-salarial-2025-1 repository, Eduardo Gripp developed and refined data-driven analytics for salary disparity research. He engineered robust data cleaning and preprocessing utilities in Python and Pandas, implemented exploratory and comparative model analyses using LightGBM and Scikit-learn, and enhanced reporting with Matplotlib and Seaborn visualizations. His work included integrating auxiliary datasets, automating model deployment, and maintaining comprehensive documentation. By focusing on reproducibility, code organization, and clear model interpretation, Eduardo delivered a maintainable analytics pipeline that improved data quality, supported decision-making, and enabled scalable, transparent reporting for project stakeholders.
June 2025 monthly summary for ICEI-PUC-Minas-PPL-CDIA/ppl-cd-pcd-sist-int-2025-1-grupo2-disparidade-salarial-2025-1 focusing on delivering data-driven analysis enhancements for the 3rd data-driven question, expanding model interpretation and comparative analysis capabilities, and building robust data preparation utilities, while improving deployment readiness, documentation, and repository hygiene.
June 2025 monthly summary for ICEI-PUC-Minas-PPL-CDIA/ppl-cd-pcd-sist-int-2025-1-grupo2-disparidade-salarial-2025-1 focusing on delivering data-driven analysis enhancements for the 3rd data-driven question, expanding model interpretation and comparative analysis capabilities, and building robust data preparation utilities, while improving deployment readiness, documentation, and repository hygiene.
Monthly summary for ICEI-PUC-Minas-PPL-CDIA project (May 2025): Focused on delivering data-oriented model work, improving reliability of the Pergunta Orientada a Dados (POD) flow, and enhancing documentation and visuals for model results. Key achievements span feature delivery, bug fixes, data cleaning/EDA, and repository hygiene that collectively improve analyst productivity and stakeholder confidence in model outputs.
Monthly summary for ICEI-PUC-Minas-PPL-CDIA project (May 2025): Focused on delivering data-oriented model work, improving reliability of the Pergunta Orientada a Dados (POD) flow, and enhancing documentation and visuals for model results. Key achievements span feature delivery, bug fixes, data cleaning/EDA, and repository hygiene that collectively improve analyst productivity and stakeholder confidence in model outputs.
April 2025 monthly summary: Delivered substantial data-oriented enhancements for the salary disparity project, including new data orientation framework, expanded visual analytics, data cleaning infrastructure, and modeling artifacts. Implemented the third data-oriented question, organized graphics, and introduced multi-dimensional visualizations to drive actionable insights. Fixed critical data presentation issues and strengthened documentation and repository scaffolding, supporting scalable analytics across main and auxiliary bases.
April 2025 monthly summary: Delivered substantial data-oriented enhancements for the salary disparity project, including new data orientation framework, expanded visual analytics, data cleaning infrastructure, and modeling artifacts. Implemented the third data-oriented question, organized graphics, and introduced multi-dimensional visualizations to drive actionable insights. Fixed critical data presentation issues and strengthened documentation and repository scaffolding, supporting scalable analytics across main and auxiliary bases.
March 2025 – Salary disparity analysis project (ICEI-PUC-Minas-PPL-CDIA/ppl-cd-pcd-sist-int-2025-1-grupo2-disparidade-salarial-2025-1). Delivered three core improvements focused on documentation quality, reproducible analytics, and deeper data insights to support decision-making and reporting efficiency.
March 2025 – Salary disparity analysis project (ICEI-PUC-Minas-PPL-CDIA/ppl-cd-pcd-sist-int-2025-1-grupo2-disparidade-salarial-2025-1). Delivered three core improvements focused on documentation quality, reproducible analytics, and deeper data insights to support decision-making and reporting efficiency.
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