DataBytes-Organisation/Katabatic
May 2025 – May 2025
1 Month active
Languages Used
Python
Technical Skills
Data AnalysisData GenerationMachine LearningModel EvaluationPython
LAKMAL AMILA
PROFILE
Lakmal Amila
Amila developed an end-to-end synthetic data generation pipeline for the DataBytes-Organisation/Katabatic repository, focusing on leveraging GANBLR to create and evaluate artificial datasets. Using Python and machine learning techniques, Amila trained the GANBLR model, generated synthetic samples, and performed distribution-level comparisons to real data to ensure alignment. To assess the practical value of the synthetic data, Amila implemented a classifier evaluation harness, training and testing models such as Logistic Regression, MLP, Random Forest, and XGBoost on the generated data. This work demonstrated depth in data analysis, model evaluation, and data generation, providing a robust foundation for downstream machine learning tasks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
47,925
Activity Months1
Your Network
106 people
Work History
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for DataBytes-Organisation/Katabatic. Focused on synthetic data generation with GANBLR and classifier evaluation. Implemented an end-to-end GANBLR-based synthetic data generation pipeline, trained the model, generated synthetic samples, and performed distribution-level comparisons against real data. Established a classifier evaluation harness to assess downstream usefulness by training and evaluating Logistic Regression, MLP, Random Forest, and XGBoost on synthetic data. All changes committed under the commit hash 3a1d85336c7473af6d197f7c35efc204647e086a with the message 'Amila_Ganblr'.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary for DataBytes-Organisation/Katabatic. Focused on synthetic data generation with GANBLR and classifier evaluation. Implemented an end-to-end GANBLR-based synthetic data generation pipeline, trained the model, generated synthetic samples, and performed distribution-level comparisons against real data. Established a classifier evaluation harness to assess downstream usefulness by training and evaluating Logistic Regression, MLP, Random Forest, and XGBoost on synthetic data. All changes committed under the commit hash 3a1d85336c7473af6d197f7c35efc204647e086a with the message 'Amila_Ganblr'.
May 2025
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data AnalysisData GenerationMachine LearningModel EvaluationPython
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