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
Edsel Tan developed and deployed a robust ROI analytics pipeline for the Yihe-Harry/DSA3101-Group-Project repository, focusing on marketing campaign data. He engineered a modular data cleaning process in Python using pandas and numpy, then refactored it into a reusable class to streamline preprocessing. Edsel implemented feature engineering for ROI metrics, integrated US holiday indicators, and trained XGBoost regression models with cross-validation and hyperparameter tuning. He containerized the workflow with Docker, improved project structure, and delivered a Streamlit app for ROI prediction. His work emphasized reproducibility, maintainability, and clear documentation, enabling faster, more reliable ROI estimation and business analysis.
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 people
Work 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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