Gabriel Chaves contributed to the ICEI-PUC-Minas-PPL-CDIA/ppl-cd-pcd-sist-int-2025-1-grupo2-disparidade-salarial-2025-1 repository by developing a robust data analysis and modeling pipeline for salary disparity research. He engineered end-to-end workflows for data cleaning, exploratory analysis, and machine learning, leveraging Python, Pandas, and scikit-learn. Gabriel implemented Random Forest models for both regression and classification, producing interpretable results and visualizations to inform business decisions. His work included structured documentation, asset management, and repository hygiene, ensuring reproducibility and maintainability. The depth of his engineering addressed data quality, model reliability, and stakeholder reporting, supporting efficient, data-driven insights into salary disparities.
June 2025 performance summary for the disparity-in-salaries analysis project. Delivered a cohesive set of modeling, documentation, and repository hygiene improvements that enhance reproducibility, onboarding, and business value. Implemented a structured data-question workflow (Pergunta Orientada a Dados) with initial setup and ongoing documentation, and expanded model explanations with clear Renamer variants (Regressor/Classifier). Advanced modeling work includes Model 2 using RandomForestClassifier with results and interpretation, along with supporting code and explanations. Restructured documentation for easier navigation and stronger governance, including a new project README, a clarified assets section, and a move from IMAGENS.md to REPORT.md. Significant asset and directory cleanup to improve maintainability and reduce confusion, including deletion of outdated reports and images, and updates to CITATION.cff with ORCID. Visualization capabilities were enhanced with exploratory charts and Model 2 graphics, plus interpretations of Model results. Introduced new modeling scripts (Codigo_Modelo_1.py and Codigo_Modelo_2.py) and updated analytical documentation (Explicação_Modelo_1.md, Explicação_Modelo_2). File uploads for assets and robust naming conventions were completed to support consistency across analyses.
June 2025 performance summary for the disparity-in-salaries analysis project. Delivered a cohesive set of modeling, documentation, and repository hygiene improvements that enhance reproducibility, onboarding, and business value. Implemented a structured data-question workflow (Pergunta Orientada a Dados) with initial setup and ongoing documentation, and expanded model explanations with clear Renamer variants (Regressor/Classifier). Advanced modeling work includes Model 2 using RandomForestClassifier with results and interpretation, along with supporting code and explanations. Restructured documentation for easier navigation and stronger governance, including a new project README, a clarified assets section, and a move from IMAGENS.md to REPORT.md. Significant asset and directory cleanup to improve maintainability and reduce confusion, including deletion of outdated reports and images, and updates to CITATION.cff with ORCID. Visualization capabilities were enhanced with exploratory charts and Model 2 graphics, plus interpretations of Model results. Introduced new modeling scripts (Codigo_Modelo_1.py and Codigo_Modelo_2.py) and updated analytical documentation (Explicação_Modelo_1.md, Explicação_Modelo_2). File uploads for assets and robust naming conventions were completed to support consistency across analyses.
In May 2025, delivered end-to-end enhancements for the second data-oriented question of the salary disparity analysis project. Key features include dataset CSV creation, cleanup, and naming corrections; addition of a base auxiliar and implementation of a RandomForest_salarios model with induction and results; graphical and exploratory data analysis assets for the second question; data cleaning workflow improvements and explanatory updates; repository hygiene improvements (gitignore updates, obsolete data removal), and documentation enhancements. These changes improve data quality, model reliability, and stakeholder-facing visuals, enabling faster extraction of business insights on salary disparities and stronger data-driven decision making.
In May 2025, delivered end-to-end enhancements for the second data-oriented question of the salary disparity analysis project. Key features include dataset CSV creation, cleanup, and naming corrections; addition of a base auxiliar and implementation of a RandomForest_salarios model with induction and results; graphical and exploratory data analysis assets for the second question; data cleaning workflow improvements and explanatory updates; repository hygiene improvements (gitignore updates, obsolete data removal), and documentation enhancements. These changes improve data quality, model reliability, and stakeholder-facing visuals, enabling faster extraction of business insights on salary disparities and stronger data-driven decision making.
April 2025: Delivered an end-to-end salary data pipeline and insights for disparity analysis in the salary domain. Implemented a Data Cleaning and Standardization Pipeline across multiple sources with normalization of experience and seniority, job-code mapping, outlier removal, one-hot encoding, and export of cleaned datasets for analytics. Conducted Exploratory Data Analysis and produced salary insights by experience, seniority, education, and region to inform compensation decisions. Strengthened documentation and governance with structured guidance and removal of outdated assets, improving reproducibility and maintainability. These efforts collectively improved data quality, operational efficiency, and business value for salary disparity initiatives.
April 2025: Delivered an end-to-end salary data pipeline and insights for disparity analysis in the salary domain. Implemented a Data Cleaning and Standardization Pipeline across multiple sources with normalization of experience and seniority, job-code mapping, outlier removal, one-hot encoding, and export of cleaned datasets for analytics. Conducted Exploratory Data Analysis and produced salary insights by experience, seniority, education, and region to inform compensation decisions. Strengthened documentation and governance with structured guidance and removal of outdated assets, improving reproducibility and maintainability. These efforts collectively improved data quality, operational efficiency, and business value for salary disparity initiatives.
March 2025 — Documentation and data-model stabilization for ICEI-PUC project. Delivered consolidated README with installation/setup/usage notes, updated reporting documentation to reflect new formats and examples, and cleaned the dataset schema by removing deprecated attributes across P2_a, P2_b, P2_c, P3_b, and P5_b. Initiated Exploratory Data Analysis on the Base Principal to establish baseline insights for analytics. These efforts improve onboarding speed, standardize reporting, and strengthen data quality for BI and downstream analytics.
March 2025 — Documentation and data-model stabilization for ICEI-PUC project. Delivered consolidated README with installation/setup/usage notes, updated reporting documentation to reflect new formats and examples, and cleaned the dataset schema by removing deprecated attributes across P2_a, P2_b, P2_c, P3_b, and P5_b. Initiated Exploratory Data Analysis on the Base Principal to establish baseline insights for analytics. These efforts improve onboarding speed, standardize reporting, and strengthen data quality for BI and downstream analytics.
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