SpikyCherry/DSA3101_group9
Apr 2025 – Apr 2025
1 Month active
Languages Used
Python
Technical Skills
Data PreprocessingData SegmentationModel Training
Yuqing Shen
PROFILE
Yuqing Shen
During April 2025, Yuqing Shen focused on stabilizing the data pipeline for the SpikyCherry/DSA3101_group9 repository by resolving a critical bug that prevented proper data loading for the BankChurners dataset. By correcting the dataset path in the preprocessing and training scripts, Yuqing restored end-to-end functionality for data ingestion, preprocessing, and model training. The work included developing a new notebook to streamline preprocessing, segmentation, and experimentation, which improved reproducibility and reduced debugging time. Leveraging Python for data handling and model training, Yuqing’s contributions enhanced the reliability of the machine learning workflow, though the scope was limited to bug fixes and pipeline improvements.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
1Total
Bugs
1
Commits
1
Features
0
Lines of code
4
Activity Months1
Your Network
8 people
Work History
April 2025
1 Commits
Apr 1, 2025April 2025 – SpikyCherry/DSA3101_group9: Key data pipeline stabilization and Q5 notebook enhancements. Key features delivered: corrected BankChurners dataset path to data/BankChurners.csv, enabling reliable data loading, preprocessing, and model training. Notable addition: preprocessing, training, and segmentation notebook for Q5 to accelerate experimentation and reproducibility. Major bugs fixed: resolved incorrect file path references in preprocessing and training scripts that blocked data loading. Overall impact and accomplishments: restored end-to-end capability for BankChurners experiments, improved pipeline reliability, and reduced debugging time for data ingestion. Technologies/skills demonstrated: Python-based data pipelines, dataset handling, notebook-driven experimentation, debugging, and ML workflow integration with version control.
1 Commits
Apr 1, 2025April 2025 – SpikyCherry/DSA3101_group9: Key data pipeline stabilization and Q5 notebook enhancements. Key features delivered: corrected BankChurners dataset path to data/BankChurners.csv, enabling reliable data loading, preprocessing, and model training. Notable addition: preprocessing, training, and segmentation notebook for Q5 to accelerate experimentation and reproducibility. Major bugs fixed: resolved incorrect file path references in preprocessing and training scripts that blocked data loading. Overall impact and accomplishments: restored end-to-end capability for BankChurners experiments, improved pipeline reliability, and reduced debugging time for data ingestion. Technologies/skills demonstrated: Python-based data pipelines, dataset handling, notebook-driven experimentation, debugging, and ML workflow integration with version control.
April 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Data PreprocessingData SegmentationModel Training
Repositories Contributed To
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