Over four months, contributed to the ICEI-PUC-Minas-PPL-CDIA/ppl-cd-pcd-sist-int-2025-1-grupo2-disparidade-salarial-2025-1 repository by developing data-driven analytics and model workflows addressing salary disparity. Built and refined Python-based pipelines for data cleaning, exploratory analysis, and predictive modeling using tools such as Pandas, Scikit-learn, and LightGBM. Enhanced reporting and documentation to support reproducibility and stakeholder understanding, integrating visualizations and comparative model interpretation. Improved repository structure, implemented utilities for data preparation, and enabled remote model deployment. Addressed bugs and maintained code hygiene, ensuring reliable model outputs and scalable analytics. The work emphasized robust data science practices and clear technical communication throughout.
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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