DrAlzahraniProjects/csusb_fall2024_cse6550_team1
Nov 2024 – Nov 2024
1 Month active
Languages Used
CSSPython
Technical Skills
Data AnalysisData EngineeringData VisualizationDatabase ManagementEmbeddingsMilvus
tejaswi-142
PROFILE
Tejaswi-142
Contributed to the DrAlzahraniProjects/csusb_fall2024_cse6550_team1 repository by delivering analytics and data infrastructure enhancements. Developed a Confusion Matrix visualization for the Metrics Dashboard, improving model performance transparency through a styled UI table and organized metric sections. Addressed a backend issue in SQL query parameterization to ensure reliable database resets. Modernized data presentation by updating Pandas styling methods for compatibility with current libraries. Refactored the vector store backend to use PyMilvus, introducing explicit schema definition, indexing, and data insertion, along with text hashing and embedding utilities. Work leveraged Python, SQL, and Streamlit, emphasizing maintainability and robust data engineering practices.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
4Total
Bugs
1
Commits
4
Features
3
Lines of code
199
Activity Months1
Your Network
43 peopleWork History
November 2024
4 Commits • 3 Features
Nov 1, 2024November 2024 milestones: Delivered key analytics improvements, stabilized data processing, and modernized data presentation, aligning engineering work with business value. Major deliverables include a new Confusion Matrix visualization in the Metrics Dashboard, a performance metrics backend bug fix, modernization of Pandas styling to ensure future compatibility, and a refactor of the vector store backend using PyMilvus with explicit schema, indexing, and data insertion. These changes improve model performance visibility, reliability of reset flows, compatibility with current data tooling, and robustness of vector storage.
4 Commits • 3 Features
Nov 1, 2024November 2024 milestones: Delivered key analytics improvements, stabilized data processing, and modernized data presentation, aligning engineering work with business value. Major deliverables include a new Confusion Matrix visualization in the Metrics Dashboard, a performance metrics backend bug fix, modernization of Pandas styling to ensure future compatibility, and a refactor of the vector store backend using PyMilvus with explicit schema, indexing, and data insertion. These changes improve model performance visibility, reliability of reset flows, compatibility with current data tooling, and robustness of vector storage.
November 2024
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture92.6%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSSPython
Technical Skills
Data AnalysisData EngineeringData VisualizationDatabase ManagementEmbeddingsMilvusPandasSQLStreamlitVector DatabasesWeb Development
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