DataBytes-Organisation/Katabatic
Apr 2025 – Apr 2025
1 Month active
Languages Used
CSSHTMLJavaScriptPython
Technical Skills
Backend DevelopmentData ScienceFlaskFrontend DevelopmentMachine LearningWeb Development
palak
PROFILE
Palak
Palak Bedi developed a Flask-based frontend UI for the palakbedi4_gamblr synthetic data generation platform, hosted in the DataBytes-Organisation/Katabatic repository. The project established modular routes for Home, About, Services, and Contact pages, as well as dedicated interfaces for models such as Glanblr, CTGAN, and Meg. Palak integrated Python and JavaScript to enable workflows for data preprocessing uploads, model training, synthetic data generation, and visualization, connecting the frontend to backend machine learning libraries. The architecture was designed for scalability, supporting future model integrations and streamlined user workflows. This work demonstrated depth in both backend and frontend web development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
1,530
Activity Months1
Your Network
18 people
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025Delivered end-to-end Flask frontend UI for the palakbedi4_gamblr synthetic data generation platform, providing a cohesive entry point (Home, About, Services, Contact) and model-specific interfaces. Implemented modular routes and interfaces to manage data preprocessing (uploads), model training, synthetic data generation, and visualization, with backend integration to ML libraries for execution across multiple models (Glanblr, CTGAN, Meg). Established a scalable Flask architecture with clean API endpoints to support future model integrations and surface-ready workflows for users and stakeholders.
1 Commits • 1 Features
Apr 1, 2025Delivered end-to-end Flask frontend UI for the palakbedi4_gamblr synthetic data generation platform, providing a cohesive entry point (Home, About, Services, Contact) and model-specific interfaces. Implemented modular routes and interfaces to manage data preprocessing (uploads), model training, synthetic data generation, and visualization, with backend integration to ML libraries for execution across multiple models (Glanblr, CTGAN, Meg). Established a scalable Flask architecture with clean API endpoints to support future model integrations and surface-ready workflows for users and stakeholders.
April 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSSHTMLJavaScriptPython
Technical Skills
Backend DevelopmentData ScienceFlaskFrontend DevelopmentMachine LearningWeb Development
Repositories Contributed To
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