mauricioantelis/TC1002S
Mar 2025 – Mar 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Accuracy ScoreClassificationConfusion MatrixData AnalysisData PreprocessingData Science
Dhali
PROFILE
Dhali
Carlos Dhali worked on the mauricioantelis/TC1002S repository, focusing on restructuring data assets and developing educational machine learning notebooks. He reorganized student information files to improve data governance and maintainability, enhancing the clarity of the project’s structure. Carlos created targeted Jupyter notebooks for Iris dataset exploration and supervised classification, applying Python, Pandas, and Scikit-learn for data preprocessing, visualization, and model evaluation. His disciplined use of version control and explicit commit messages supported reproducibility and onboarding. The work demonstrated a solid grasp of data science workflows, though the scope was limited to two features and did not involve bug fixes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
25,057
Activity Months1
Your Network
27 people
Work History
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for the mauricioantelis/TC1002S repository. Key accomplishments include restructuring and organizing data assets to improve maintainability and educational value, along with the creation of targeted notebooks for Iris data exploration and ML classification. There were no major bugs fixed in this period. Overall impact includes improved data governance, clearer project structure, and practical ML educational tooling that supports faster onboarding and reproducible experiments. Technologies and skills demonstrated include Python notebooks, data visualization, preprocessing, supervised learning workflows, and disciplined Git version control with explicit commit messages.
4 Commits • 2 Features
Mar 1, 2025March 2025 performance summary for the mauricioantelis/TC1002S repository. Key accomplishments include restructuring and organizing data assets to improve maintainability and educational value, along with the creation of targeted notebooks for Iris data exploration and ML classification. There were no major bugs fixed in this period. Overall impact includes improved data governance, clearer project structure, and practical ML educational tooling that supports faster onboarding and reproducible experiments. Technologies and skills demonstrated include Python notebooks, data visualization, preprocessing, supervised learning workflows, and disciplined Git version control with explicit commit messages.
March 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Accuracy ScoreClassificationConfusion MatrixData AnalysisData PreprocessingData ScienceData VisualizationFile ManagementJupyter NotebookLogistic RegressionMachine LearningMatplotlibModel EvaluationNumPyPandas
Repositories Contributed To
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