CUAI-CAU/2025_Basic_Track_Assignment
Mar 2025 – May 2025
2 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Array OperationsCross-ValidationCross-validationData AnalysisData ClusteringData Encoding
lgy7721
PROFILE
Lgy7721
Over a two-month period, contributed to the CUAI-CAU/2025_Basic_Track_Assignment repository by developing a suite of Jupyter Notebooks focused on foundational data science and machine learning workflows. Built educational content covering data loading, preprocessing, model training, evaluation, and visualization, with practical examples using datasets like Iris and Titanic. Leveraged Python, NumPy, Pandas, and scikit-learn to implement reproducible pipelines for clustering, regression, and classification tasks. Established a version-controlled, notebook-based workflow that supports scalable curriculum development and streamlined onboarding for learners. The work emphasized hands-on, end-to-end machine learning assignments, enabling reproducibility and assessment readiness without reported major bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
16Total
Bugs
0
Commits
16
Features
3
Lines of code
14,714
Activity Months2
Your Network
24 peopleWork History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025: Implemented ML assignment notebooks in CUAI-CAU/2025_Basic_Track_Assignment to deliver practical ML learning artifacts. Focused on end-to-end coverage of data loading, model training, evaluation, and visualization across three notebooks for accuracy metrics, decision tree visualization and overfitting analysis, and regression trees. This work enhances learner onboarding, assessment readiness, and reproducibility.
1 Commits • 1 Features
May 1, 2025May 2025: Implemented ML assignment notebooks in CUAI-CAU/2025_Basic_Track_Assignment to deliver practical ML learning artifacts. Focused on end-to-end coverage of data loading, model training, evaluation, and visualization across three notebooks for accuracy metrics, decision tree visualization and overfitting analysis, and regression trees. This work enhances learner onboarding, assessment readiness, and reproducibility.
May 2025
March 2025
15 Commits • 2 Features
Mar 1, 2025March 2025 – CUAI-CAU/2025_Basic_Track_Assignment: Delivered two feature-rich notebook suites establishing foundational data science skills and end-to-end ML workflows. Features delivered: NumPy and Pandas Fundamentals Notebooks with data loading and basic manipulation; Machine Learning Notebooks and Tutorials covering data loading, preprocessing, model training/evaluation, clustering, regression, and classification across Iris, Titanic, and Kaggle tasks. Major bugs fixed: No explicit major bugs reported; ongoing refinements and asset consistency updates were performed. Overall impact: Ready-to-use educational content enabling faster onboarding, reusable curriculum for scalable learning, and demonstrable value to learners and instructors. Technologies/skills demonstrated: Python, NumPy, Pandas, scikit-learn-like ML workflows, notebook-based pedagogy, data loading pipelines, visualization assets.
March 2025
15 Commits • 2 Features
Mar 1, 2025March 2025 – CUAI-CAU/2025_Basic_Track_Assignment: Delivered two feature-rich notebook suites establishing foundational data science skills and end-to-end ML workflows. Features delivered: NumPy and Pandas Fundamentals Notebooks with data loading and basic manipulation; Machine Learning Notebooks and Tutorials covering data loading, preprocessing, model training/evaluation, clustering, regression, and classification across Iris, Titanic, and Kaggle tasks. Major bugs fixed: No explicit major bugs reported; ongoing refinements and asset consistency updates were performed. Overall impact: Ready-to-use educational content enabling faster onboarding, reusable curriculum for scalable learning, and demonstrable value to learners and instructors. Technologies/skills demonstrated: Python, NumPy, Pandas, scikit-learn-like ML workflows, notebook-based pedagogy, data loading pipelines, visualization assets.
Activity
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Quality Metrics
Correctness81.4%
Maintainability78.8%
Architecture76.2%
Performance76.4%
AI Usage22.4%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Array OperationsCross-ValidationCross-validationData AnalysisData ClusteringData EncodingData ExplorationData ManipulationData PreprocessingData ScienceData VisualizationDecision TreesElasticNet RegressionFeature EngineeringFeature Scaling
Repositories Contributed To
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