DataBytes-Organisation/Katabatic
May 2025 – May 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data PreprocessingDimensionality ReductionFeature EngineeringGenerative Adversarial Networks (GANs)Machine LearningPyTorch
Pasindu1234*
PROFILE
Pasindu1234*
Prabashwara developed a synthetic data generator for tabular data in the DataBytes-Organisation/Katabatic repository, focusing on privacy-preserving data generation for testing and analytics. Leveraging Python and Jupyter Notebook, Prabashwara integrated a Conditional GAN (CRGAN) model, incorporating data loading, preprocessing, and PCA-based feature engineering to ensure robust input representation. The pipeline included training and evaluating synthetic data using a Gaussian Mixture Model, verifying that generated data closely matched the original distribution. This end-to-end solution established a reusable workflow for scalable synthetic data generation, reducing reliance on real datasets and supporting broader data science initiatives within the Katabatic project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,597
Activity Months1
Your Network
18 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025Monthly Summary for 2025-05 - DataBytes-Organisation/Katabatic: Key features delivered: - Synthetic Data Generator for Tabular Data using Conditional GAN (CRGAN). Includes data loading, preprocessing, PCA-based feature engineering, and training/evaluation using a Gaussian Mixture Model to ensure the synthetic data approximates the original distribution. Enables scalable, privacy-preserving synthetic data for testing and analytics. Major bugs fixed: - None reported for this month; focus was on feature development and integration. Overall impact and accomplishments: - Provides a privacy-preserving data generation capability that reduces reliance on real data for testing and analytics, accelerating data science workflows while improving data privacy. - Establishes a reusable synthetic data generation pipeline within Katabatic, ready for broader data domains. Technologies/skills demonstrated: - Generative modeling (CRGAN), data loading/ preprocessing, PCA-based feature engineering, and GMM evaluation. - End-to-end feature integration within a production-like repository. - Collaboration/coordination evidenced by integration of Pasindu's CRGAN model (commit 211c2199e3ea09b5ba63a3a019ba5a14c259952b).
1 Commits • 1 Features
May 1, 2025Monthly Summary for 2025-05 - DataBytes-Organisation/Katabatic: Key features delivered: - Synthetic Data Generator for Tabular Data using Conditional GAN (CRGAN). Includes data loading, preprocessing, PCA-based feature engineering, and training/evaluation using a Gaussian Mixture Model to ensure the synthetic data approximates the original distribution. Enables scalable, privacy-preserving synthetic data for testing and analytics. Major bugs fixed: - None reported for this month; focus was on feature development and integration. Overall impact and accomplishments: - Provides a privacy-preserving data generation capability that reduces reliance on real data for testing and analytics, accelerating data science workflows while improving data privacy. - Establishes a reusable synthetic data generation pipeline within Katabatic, ready for broader data domains. Technologies/skills demonstrated: - Generative modeling (CRGAN), data loading/ preprocessing, PCA-based feature engineering, and GMM evaluation. - End-to-end feature integration within a production-like repository. - Collaboration/coordination evidenced by integration of Pasindu's CRGAN model (commit 211c2199e3ea09b5ba63a3a019ba5a14c259952b).
May 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data PreprocessingDimensionality ReductionFeature EngineeringGenerative Adversarial Networks (GANs)Machine LearningPyTorchScikit-learnTabular Data Analysis
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