CUAI-CAU/2025_Basic_Track_Assignment
Mar 2025 – May 2025
3 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
ClusteringData AnalysisData ManipulationData ScienceData VisualizationDecision Trees
evaseo0078
PROFILE
Evaseo0078
Over a three-month period, contributed to the CUAI-CAU/2025_Basic_Track_Assignment repository by developing four Jupyter Notebook features focused on practical data science and machine learning workflows. Built structured tutorials covering NumPy and Pandas fundamentals, supervised and unsupervised learning with Decision Trees and K-Means, and regression analysis using Ridge, Lasso, and ElasticNet. Delivered a reproducible PCA notebook demonstrating dimensionality reduction and model evaluation on the Iris dataset. Emphasized reproducibility, clean version control, and educational clarity throughout, enabling rapid onboarding and experimentation. Leveraged Python, Scikit-learn, and Matplotlib to create reusable assets supporting data analysis, model benchmarking, and collaborative learning within the team.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
4
Lines of code
13,238
Activity Months3
Your Network
19 peopleWork History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 performance summary for CUAI-CAU project: delivered a self-contained PCA Notebook illustrating dimensionality reduction on Iris data, with end-to-end data scaling, PCA transformation, visualization, and a model-accuracy comparison; laid groundwork for credit-card dataset PCA explorations; progress aligns with team goals for reproducible analytics and educational material.
1 Commits • 1 Features
May 1, 2025May 2025 performance summary for CUAI-CAU project: delivered a self-contained PCA Notebook illustrating dimensionality reduction on Iris data, with end-to-end data scaling, PCA transformation, visualization, and a model-accuracy comparison; laid groundwork for credit-card dataset PCA explorations; progress aligns with team goals for reproducible analytics and educational material.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment. Delivered an end-to-end Regression Model Benchmark Notebook comparing Ridge, Lasso, and ElasticNet. The notebook covers data loading, preprocessing, model training, hyperparameter tuning, and performance evaluation using RMSE and accuracy metrics, and explores scaling methods and polynomial features. This work establishes a reproducible baseline for regression models to inform feature engineering and model selection.
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment. Delivered an end-to-end Regression Model Benchmark Notebook comparing Ridge, Lasso, and ElasticNet. The notebook covers data loading, preprocessing, model training, hyperparameter tuning, and performance evaluation using RMSE and accuracy metrics, and explores scaling methods and polynomial features. This work establishes a reproducible baseline for regression models to inform feature engineering and model selection.
March 2025
4 Commits • 2 Features
Mar 1, 2025March 2025 performance: Delivered two feature bundles in CUAI-CAU/2025_Basic_Track_Assignment, enabling rapid onboarding and practical data science experimentation. Implemented NumPy basics and Pandas data handling tutorials, and a Machine Learning Notebooks Bundle covering Decision Tree, K-Means, and regression workflows. All work is version-controlled, organized for reproducibility, and positioned to drive self-service learning. No major bugs fixed this month; stability improvements were achieved through structured notebooks and clean commits. This work enhances data literacy, accelerates experimentation, and creates reusable learning assets across the team.
4 Commits • 2 Features
Mar 1, 2025March 2025 performance: Delivered two feature bundles in CUAI-CAU/2025_Basic_Track_Assignment, enabling rapid onboarding and practical data science experimentation. Implemented NumPy basics and Pandas data handling tutorials, and a Machine Learning Notebooks Bundle covering Decision Tree, K-Means, and regression workflows. All work is version-controlled, organized for reproducibility, and positioned to drive self-service learning. No major bugs fixed this month; stability improvements were achieved through structured notebooks and clean commits. This work enhances data literacy, accelerates experimentation, and creates reusable learning assets across the team.
March 2025
Activity
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Quality Metrics
Correctness86.6%
Maintainability86.6%
Architecture86.6%
Performance83.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
ClusteringCross-validationData AnalysisData ManipulationData PreprocessingData ScienceData VisualizationDecision TreesDimensionality ReductionElasticNet RegressionFeature ScalingGradient DescentGrid SearchHyperparameter TuningJupyter Notebook
Repositories Contributed To
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