Yihe-Harry/DSA3101-Group-Project
Mar 2025 – Apr 2025
2 Months active
Languages Used
CSVDockerfilePythonGit IgnoreJupyter NotebookMarkdown
Technical Skills
Code OrganizationCode RefactoringCross-ValidationData AnalysisData CleaningData Engineering
edsel
PROFILE
Edsel
Contributed to the Yihe-Harry/DSA3101-Group-Project by developing an end-to-end ROI analytics pipeline, including data ingestion, cleaning, feature engineering, and predictive modeling. Built reusable data cleaning components in Python using pandas and numpy, and engineered features such as weekday indicators and ROI metrics to enhance model accuracy. Developed and tuned XGBoost regression models with cross-validation, integrating results into a Streamlit app for interactive ROI prediction. Improved deployment readiness through Dockerization and project restructuring, while maintaining comprehensive documentation and Jupyter notebooks for analysis transparency. Addressed codebase hygiene and streamlined workflows, enabling reproducible, maintainable, and business-focused ROI analytics delivery.
Overall Statistics
Feature vs Bugs
92%Features
Repository Contributions
40Total
Bugs
1
Commits
40
Features
11
Lines of code
417,240
Activity Months2
Your Network
7 peopleWork History
April 2025
21 Commits • 6 Features
Apr 1, 2025April 2025 performance highlights for Yihe-Harry/DSA3101-Group-Project. Key features delivered include ROI Prediction Streamlit App with UI improvements, B3 Notebook for B3 analysis, Dockerization readiness, B3 integration enhancements, and comprehensive documentation/README updates. Major bugs fixed include a critical line-of-code issue in the Streamlit app and repo hygiene fixes (gitignore updates and file renames). Overall impact: accelerated ROI analytics delivery, improved deployment readiness, and enhanced maintainability, enabling faster business value realization. Technologies/skills demonstrated include Python, Streamlit, Jupyter notebooks, Docker, Git/version control, and documentation practices. Business value: faster ROI estimation, reproducible deployments, and clearer ROI modeling docs.
21 Commits • 6 Features
Apr 1, 2025April 2025 performance highlights for Yihe-Harry/DSA3101-Group-Project. Key features delivered include ROI Prediction Streamlit App with UI improvements, B3 Notebook for B3 analysis, Dockerization readiness, B3 integration enhancements, and comprehensive documentation/README updates. Major bugs fixed include a critical line-of-code issue in the Streamlit app and repo hygiene fixes (gitignore updates and file renames). Overall impact: accelerated ROI analytics delivery, improved deployment readiness, and enhanced maintainability, enabling faster business value realization. Technologies/skills demonstrated include Python, Streamlit, Jupyter notebooks, Docker, Git/version control, and documentation practices. Business value: faster ROI estimation, reproducible deployments, and clearer ROI modeling docs.
April 2025
March 2025
19 Commits • 5 Features
Mar 1, 2025March 2025 monthly summary for Yihe-Harry/DSA3101-Group-Project highlighting key features delivered, major technical improvements, and business impact for ROI-focused analytics.
March 2025
19 Commits • 5 Features
Mar 1, 2025March 2025 monthly summary for Yihe-Harry/DSA3101-Group-Project highlighting key features delivered, major technical improvements, and business impact for ROI-focused analytics.
Activity
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Quality Metrics
Correctness88.8%
Maintainability89.6%
Architecture85.4%
Performance83.6%
AI Usage20.6%
Skills & Technologies
Programming Languages
CSVDockerfileGit IgnoreJupyter NotebookMarkdownPython
Technical Skills
Code OrganizationCode RefactoringCross-ValidationData AnalysisData CleaningData EngineeringData ModelingData PreprocessingData ScienceData VisualizationDevOpsDockerDocumentationFeature EngineeringFile Management
Repositories Contributed To
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