mauricioantelis/TC1002S
Mar 2025 – Mar 2025
1 Month active
Languages Used
CSVJupyter NotebookPythonText
Technical Skills
Data AnalysisData VisualizationDocumentationJupyter NotebookMachine LearningMatplotlib
DanteGuzmánR
PROFILE
Danteguzmánr
Developed two educational assets for the mauricioantelis/TC1002S repository, focusing on onboarding and hands-on machine learning practice. Delivered Jupyter notebooks that guide users through data exploration and visualization for the cartwheel, iris, and digits datasets, utilizing Python, Pandas, and Matplotlib to process CSV data and generate clear visuals. Expanded the repository’s machine learning coverage by implementing classification and clustering experiments on the Iris dataset, supporting reproducibility and knowledge transfer through detailed documentation and explicit commit history. The work emphasized clarity and auditability, providing evaluable activity setups and supporting learners in understanding both data analysis and machine learning workflows.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
5,039
Activity Months1
Your Network
29 peopleWork History
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025: Delivered two key educational assets for mauricioantelis/TC1002S that enhance learner onboarding, reproducibility, and hands-on ML practice. Implemented Educational Activity Setup and Data Exploration Notebooks for cartwheel, iris, and digits datasets with CSV data and visuals, accompanied by an evaluable activity readme. Expanded ML coverage with Iris Notebooks for classification and clustering experiments. All work is traceable through explicit commits, supporting auditability and knowledge transfer. Technologies demonstrated include Python data science stack, Jupyter notebooks, data exploration, data visualization, and ML experimentation.
4 Commits • 2 Features
Mar 1, 2025March 2025: Delivered two key educational assets for mauricioantelis/TC1002S that enhance learner onboarding, reproducibility, and hands-on ML practice. Implemented Educational Activity Setup and Data Exploration Notebooks for cartwheel, iris, and digits datasets with CSV data and visuals, accompanied by an evaluable activity readme. Expanded ML coverage with Iris Notebooks for classification and clustering experiments. All work is traceable through explicit commits, supporting auditability and knowledge transfer. Technologies demonstrated include Python data science stack, Jupyter notebooks, data exploration, data visualization, and ML experimentation.
March 2025
Activity
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Quality Metrics
Correctness87.4%
Maintainability85.0%
Architecture85.0%
Performance85.0%
AI Usage35.0%
Skills & Technologies
Programming Languages
CSVJupyter NotebookPythonText
Technical Skills
Data AnalysisData VisualizationDocumentationJupyter NotebookMachine LearningMatplotlibNumpyPandasPythonScikit-learnSeaborn
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