SpikyCherry/DSA3101_group9
Apr 2025 – Apr 2025
1 Month active
Languages Used
CSVJupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData EngineeringData PreprocessingFeature EngineeringMachine Learning
guo wenjin
PROFILE
Guo Wenjin
Guo Wei Jie developed an end-to-end banking marketing machine learning model pipeline for the SpikyCherry/DSA3101_group9 repository, focusing on robust data workflows and reproducible experimentation. Leveraging Python, Pandas, and Scikit-learn, Guo established a comprehensive process encompassing dataset setup, data cleaning, preprocessing, feature engineering, encoding, and model training integration. The work included refactoring legacy scripts, standardizing preprocessing and analysis routines, and integrating modular training scripts to ensure consistent data handling across all stages. This approach enabled reliable model prototyping and evaluation, improved maintainability, and supported governance-ready experimentation, demonstrating depth in data engineering and machine learning pipeline construction within a one-month period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
12,785
Activity Months1
Your Network
8 people
Work History
April 2025
4 Commits • 1 Features
Apr 1, 2025April 2025 — SpikyCherry/DSA3101_group9: Delivered an end-to-end Banking Marketing ML Model Pipeline and strengthened code quality and experimentation capabilities. Key deliverables include the setup of a complete data workflow for model training and exploration, encompassing dataset setup, preprocessing, feature engineering, encoding, and training script integration. The work ensures consistent data handling across steps, enabling reliable model prototyping and evaluation.
4 Commits • 1 Features
Apr 1, 2025April 2025 — SpikyCherry/DSA3101_group9: Delivered an end-to-end Banking Marketing ML Model Pipeline and strengthened code quality and experimentation capabilities. Key deliverables include the setup of a complete data workflow for model training and exploration, encompassing dataset setup, preprocessing, feature engineering, encoding, and training script integration. The work ensures consistent data handling across steps, enabling reliable model prototyping and evaluation.
April 2025
Activity
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Quality Metrics
Correctness82.6%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
CSVJupyter NotebookPython
Technical Skills
Data AnalysisData CleaningData EngineeringData PreprocessingFeature EngineeringMachine LearningPandasPython ScriptingScikit-learn
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