Yihe-Harry/DSA3101-Group-Project
Mar 2025 – Apr 2025
2 Months active
Languages Used
CSVDockerfileMarkdownPythonJupyter Notebook
Technical Skills
API DevelopmentAd OptimizationCode CleanupCustomer SegmentationData AnalysisData Cleaning
Eugjk
PROFILE
Eugjk
Contributed to the Yihe-Harry/DSA3101-Group-Project by developing real-time customer segmentation APIs, a Streamlit-based CTR campaign optimizer, and robust data provisioning pipelines for machine learning workflows. Leveraged Python, FastAPI, and Docker to deliver scalable, containerized solutions supporting dynamic recommendations and automated model retraining. Enhanced data management by reorganizing datasets, aligning structures across project modules, and cleaning legacy references to ensure reproducibility and deployment reliability. Improved repository maintainability through codebase cleanup, documentation updates, and asset management. Integrated Flask into Dockerized environments to streamline deployment. Focused on maintainable, production-ready engineering practices that strengthened data-driven experimentation and facilitated team onboarding and collaboration.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
43Total
Bugs
1
Commits
43
Features
10
Lines of code
662,313
Activity Months2
Your Network
7 peopleWork History
April 2025
24 Commits • 5 Features
Apr 1, 2025April 2025 monthly summary for Yihe-Harry/DSA3101-Group-Project: Delivered data-management overhaul, container readiness, and documentation improvements that strengthen reproducibility, deployment reliability, and team onboarding. Key dataset reorganization across A1/API/A3, extensive cleanup of unused references and directories, and alignment of datasets to current workflows. Documentation and notebook updates, cluster naming refinements, and batch asset uploads improved project clarity. Dockerfile enhancement enabling Flask-powered container execution reduced deployment friction and accelerated environment parity.
24 Commits • 5 Features
Apr 1, 2025April 2025 monthly summary for Yihe-Harry/DSA3101-Group-Project: Delivered data-management overhaul, container readiness, and documentation improvements that strengthen reproducibility, deployment reliability, and team onboarding. Key dataset reorganization across A1/API/A3, extensive cleanup of unused references and directories, and alignment of datasets to current workflows. Documentation and notebook updates, cluster naming refinements, and batch asset uploads improved project clarity. Dockerfile enhancement enabling Flask-powered container execution reduced deployment friction and accelerated environment parity.
April 2025
March 2025
19 Commits • 5 Features
Mar 1, 2025March 2025 monthly summary for Yihe-Harry/DSA3101-Group-Project highlighting business value and technical achievements across the feature set. Key data infrastructure, real-time experimentation, and customer segmentation capabilities were delivered with a focus on reliability, scalability, and maintainability. Notable improvements include ML-ready data provisioning, a Streamlit-based real-time CTR campaign optimizer with containerization, a real-time segmentation API with CRUD and automatic retraining, a lifecycle scaffold for API management, segmentation modeling analytics, and repo hygiene to reduce technical debt.
March 2025
19 Commits • 5 Features
Mar 1, 2025March 2025 monthly summary for Yihe-Harry/DSA3101-Group-Project highlighting business value and technical achievements across the feature set. Key data infrastructure, real-time experimentation, and customer segmentation capabilities were delivered with a focus on reliability, scalability, and maintainability. Notable improvements include ML-ready data provisioning, a Streamlit-based real-time CTR campaign optimizer with containerization, a real-time segmentation API with CRUD and automatic retraining, a lifecycle scaffold for API management, segmentation modeling analytics, and repo hygiene to reduce technical debt.
Activity
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Quality Metrics
Correctness91.8%
Maintainability91.6%
Architecture90.4%
Performance90.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVDockerfileJupyter NotebookMarkdownPython
Technical Skills
API DevelopmentAPI DocumentationAd OptimizationCode CleanupCode OrganizationCodebase CleanupCustomer SegmentationData AnalysisData CleaningData EngineeringData ManagementData PreparationData ScienceData VisualizationDocker
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