H6WU6R/DSA3101-Group-4
Mar 2025 – Apr 2025
2 Months active
Languages Used
CSVMarkdownPython
Technical Skills
Customer Lifetime Value PredictionCustomer SegmentationData AnalysisData EngineeringData ScienceData Visualization
wenwener0119
PROFILE
Wenwener0119
Over a two-month period, contributed to the H6WU6R/DSA3101-Group-4 repository by developing a customer segmentation and CLV prediction pipeline for marketing analytics. Leveraging Python, Pandas, and Scikit-learn, implemented K-Means clustering and data preprocessing workflows to support ROI evaluation and campaign optimization. Enhanced repository structure through refactoring, improved data path management, and added comprehensive documentation to streamline onboarding and reproducibility. Delivered features such as a Customer Churn dataset, ROI maximization guides, and end-to-end data handling scripts. Focus remained on maintainability and operational readiness, with no critical bugs reported and all work oriented toward enabling scalable, data-driven business insights.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
49Total
Bugs
0
Commits
49
Features
9
Lines of code
11,727
Activity Months2
Your Network
84 peopleShared Repositories
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Work History
April 2025
20 Commits • 2 Features
Apr 1, 2025April 2025 (H6WU6R/DSA3101-Group-4): Delivered the B3 Campaign ROI Evaluation pipeline using K-Means clustering for customer segmentation and CLV prediction, with end-to-end data preprocessing, model training, data handling, and visualization. Refined data paths, added final data scaffolding, and updated inputs. Documentation and project structure were improved, including renaming ROI-related scripts to B3_main.py, README enhancements, and data dictionary updates. No major bugs fixed this month; focus was on feature delivery, maintainability, and onboarding to enable data-driven ROI insights and scalable campaign optimization.
20 Commits • 2 Features
Apr 1, 2025April 2025 (H6WU6R/DSA3101-Group-4): Delivered the B3 Campaign ROI Evaluation pipeline using K-Means clustering for customer segmentation and CLV prediction, with end-to-end data preprocessing, model training, data handling, and visualization. Refined data paths, added final data scaffolding, and updated inputs. Documentation and project structure were improved, including renaming ROI-related scripts to B3_main.py, README enhancements, and data dictionary updates. No major bugs fixed this month; focus was on feature delivery, maintainability, and onboarding to enable data-driven ROI insights and scalable campaign optimization.
April 2025
March 2025
29 Commits • 7 Features
Mar 1, 2025Month: 2025-03. Focused on delivering clear documentation, analytics readiness, and repo hygiene to accelerate onboarding, reproducibility, and business impact. Key outcomes include comprehensive Documentation Updates (README and sections 4.x, especially 4.2 and 4.3), addition of a Customer Churn dataset for analytics experiments, and significant repo housekeeping to simplify future work. Also delivered ROI Maximisation documentation with progressive updates, introduced a CLV prediction feature, and completed README 4.3 refinements. No critical bugs reported; major work concentrated on refactoring, cleanup, and documentation to reduce maintenance overhead and improve operational readiness.
March 2025
29 Commits • 7 Features
Mar 1, 2025Month: 2025-03. Focused on delivering clear documentation, analytics readiness, and repo hygiene to accelerate onboarding, reproducibility, and business impact. Key outcomes include comprehensive Documentation Updates (README and sections 4.x, especially 4.2 and 4.3), addition of a Customer Churn dataset for analytics experiments, and significant repo housekeeping to simplify future work. Also delivered ROI Maximisation documentation with progressive updates, introduced a CLV prediction feature, and completed README 4.3 refinements. No critical bugs reported; major work concentrated on refactoring, cleanup, and documentation to reduce maintenance overhead and improve operational readiness.
Activity
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Quality Metrics
Correctness96.8%
Maintainability97.6%
Architecture96.8%
Performance95.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
CSVMarkdownPython
Technical Skills
CLV PredictionCustomer Lifetime Value PredictionCustomer SegmentationData AnalysisData EngineeringData PreprocessingData ProcessingData ScienceData VisualizationDocumentationFeature EngineeringFile ManagementFile Path ManagementFile System OperationsK-Means Clustering
Repositories Contributed To
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