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*
Developed a synthetic data generator for tabular data in the DataBytes-Organisation/Katabatic repository, focusing on privacy-preserving data generation for testing and analytics. The solution leveraged Conditional Generative Adversarial Networks (CRGAN) to create synthetic datasets that closely approximate original data distributions. The workflow included data loading, preprocessing, and PCA-based feature engineering, followed by model training and evaluation using Gaussian Mixture Models to ensure fidelity. Implemented in Python and Jupyter Notebook, the feature established an end-to-end pipeline for scalable synthetic data generation, reducing reliance on real data and supporting broader data science workflows while integrating seamlessly into the existing codebase.
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 peopleWork 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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