CUAI-CAU/2025_Basic_Track_Assignment
Mar 2025 – May 2025
3 Months active
Languages Used
Jupyter NotebookPython
Technical Skills
Cross-validationData AnalysisData ManipulationData PreprocessingData VisualizationDecision Trees
zzaerrix
PROFILE
Zzaerrix
Developed and delivered a suite of educational data science resources for the CUAI-CAU/2025_Basic_Track_Assignment repository, focusing on reproducible Jupyter notebooks and supporting assets. The work included building tutorials and templates covering NumPy, Pandas, and core machine learning workflows such as regression, classification, model evaluation, dimensionality reduction with PCA, and ensemble methods like Random Forest and XGBoost. Leveraged Python and scikit-learn to implement end-to-end examples with data preprocessing, hyperparameter tuning, and visualization using Matplotlib and Seaborn. These contributions enabled self-paced learning, accelerated prototyping, and improved onboarding by providing clear, ready-to-run artifacts aligned with the curriculum.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
10Total
Bugs
0
Commits
10
Features
5
Lines of code
28,213
Activity Months3
Your Network
19 peopleWork History
May 2025
3 Commits • 3 Features
May 1, 2025May 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment focused on delivering notebook-based ML education and evaluation templates. Three features were implemented to enhance model evaluation, dimensionality reduction visualization, and ensemble methods exploration. These contributions provide ready-to-run tutorials, improve reproducibility, and accelerate prototyping for ML workflows. No explicit bug fixes were recorded for this period.
3 Commits • 3 Features
May 1, 2025May 2025 monthly summary for CUAI-CAU/2025_Basic_Track_Assignment focused on delivering notebook-based ML education and evaluation templates. Three features were implemented to enhance model evaluation, dimensionality reduction visualization, and ensemble methods exploration. These contributions provide ready-to-run tutorials, improve reproducibility, and accelerate prototyping for ML workflows. No explicit bug fixes were recorded for this period.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025Monthly summary for 2025-04 focusing on CUAI-CAU/2025_Basic_Track_Assignment. Delivered a New ML Concepts Notebook for Regression and Classification that documents implementation and evaluation of linear regression models (Ridge, Lasso, ElasticNet) with data preprocessing and applies Logistic Regression for classification with hyperparameter tuning. This artifact provides a reproducible baseline ML workflow, enabling faster experimentation and clearer evaluation of model choices. No major bugs fixed in this scope. Overall impact: accelerates model prototyping, supports data-driven decision making, and improves onboarding for contributors. Technologies/skills demonstrated: Python, Jupyter, scikit-learn (Ridge, Lasso, ElasticNet, Logistic Regression), data preprocessing, hyperparameter tuning, model evaluation, version control.
April 2025
1 Commits • 1 Features
Apr 1, 2025Monthly summary for 2025-04 focusing on CUAI-CAU/2025_Basic_Track_Assignment. Delivered a New ML Concepts Notebook for Regression and Classification that documents implementation and evaluation of linear regression models (Ridge, Lasso, ElasticNet) with data preprocessing and applies Logistic Regression for classification with hyperparameter tuning. This artifact provides a reproducible baseline ML workflow, enabling faster experimentation and clearer evaluation of model choices. No major bugs fixed in this scope. Overall impact: accelerates model prototyping, supports data-driven decision making, and improves onboarding for contributors. Technologies/skills demonstrated: Python, Jupyter, scikit-learn (Ridge, Lasso, ElasticNet, Logistic Regression), data preprocessing, hyperparameter tuning, model evaluation, version control.
March 2025
6 Commits • 1 Features
Mar 1, 2025March 2025 summary for CUAI-CAU/2025_Basic_Track_Assignment: Delivered core Data Science course content resources, including images for early chapters and six Jupyter notebooks covering NumPy basics, Pandas basics, and introductory machine learning concepts. Commits include six file-additions across the repository: 503a801288d088472cf0389b1ddcf3588f4915c6; 1e12d385a268ca259a82548fe6e10d4e3456d822; ad4e9b0f9f440b396dd9caf6589eaf468e7fbd4c; a66622159694938f14cd954d89786488b6b23dbb; 9d2b8936e8ce6b8764e8fac23d268f9845184a6a; 4032ae135c1cbb4f914b5dabdb33523cdda782db.
6 Commits • 1 Features
Mar 1, 2025March 2025 summary for CUAI-CAU/2025_Basic_Track_Assignment: Delivered core Data Science course content resources, including images for early chapters and six Jupyter notebooks covering NumPy basics, Pandas basics, and introductory machine learning concepts. Commits include six file-additions across the repository: 503a801288d088472cf0389b1ddcf3588f4915c6; 1e12d385a268ca259a82548fe6e10d4e3456d822; ad4e9b0f9f440b396dd9caf6589eaf468e7fbd4c; a66622159694938f14cd954d89786488b6b23dbb; 9d2b8936e8ce6b8764e8fac23d268f9845184a6a; 4032ae135c1cbb4f914b5dabdb33523cdda782db.
March 2025
Activity
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Quality Metrics
Correctness89.0%
Maintainability88.0%
Architecture88.0%
Performance84.0%
AI Usage22.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
ClassificationCross-validationData AnalysisData ManipulationData PreprocessingData ScienceData VisualizationDecision TreesEnsemble LearningFeature EngineeringFeature ScalingGradient BoostingGradient DescentGrid SearchHyperparameter Tuning
Repositories Contributed To
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