SpikyCherry/DSA3101_group9
Mar 2025 – Apr 2025
2 Months active
Languages Used
CSVJupyter NotebookMarkdownPythonSQL
Technical Skills
Correlation AnalysisData AnalysisData EngineeringData PreprocessingData ScienceData Visualization
1234tjj
PROFILE
1234tjj
Developed end-to-end data science solutions for the SpikyCherry/DSA3101_group9 repository, focusing on banking analytics and customer engagement modeling. Delivered a comprehensive exploratory data analysis notebook suite, integrating correlation analysis, heatmap visualizations, and feature engineering support using Python, Pandas, and Jupyter Notebook. Built and evaluated logistic regression and random forest models with hyperparameter tuning, incorporating SHAP-based interpretability and deployment-oriented conclusions. Enhanced the data pipeline by modularizing cleaning and preprocessing workflows, implementing model persistence with pickle, and reorganizing repository artifacts for maintainability. Prioritized reproducibility, clear documentation, and streamlined data provisioning to support production-ready churn analytics and future deployment needs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
28Total
Bugs
0
Commits
28
Features
6
Lines of code
80,009
Activity Months2
Your Network
82 peopleShared Repositories
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Yuqing ShenMember
Work History
April 2025
9 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for SpikyCherry/DSA3101_group9. Focused on delivering deployment-ready features and a streamlined data pipeline. No critical bugs reported; primary work centered on feature delivery and process improvements with measurable business value. Key achievements include deployment-ready model persistence, data preparation modernization, and preprocessing enhancements. Technologies demonstrated: Python, pickle-based model serialization, modular code architecture, notebooks/scripts, and version control hygiene.
9 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for SpikyCherry/DSA3101_group9. Focused on delivering deployment-ready features and a streamlined data pipeline. No critical bugs reported; primary work centered on feature delivery and process improvements with measurable business value. Key achievements include deployment-ready model persistence, data preparation modernization, and preprocessing enhancements. Technologies demonstrated: Python, pickle-based model serialization, modular code architecture, notebooks/scripts, and version control hygiene.
April 2025
March 2025
19 Commits • 4 Features
Mar 1, 2025March 2025 performance summary for SpikyCherry/DSA3101_group9. Delivered end-to-end data science notebook solutions for banking analytics, enhanced feature engineering readiness, and improved interpretability, while ensuring reproducibility and data provisioning for churn analytics. Strengthened modeling groundwork with robust evaluation, actionable insights, and deployment-oriented conclusions.
March 2025
19 Commits • 4 Features
Mar 1, 2025March 2025 performance summary for SpikyCherry/DSA3101_group9. Delivered end-to-end data science notebook solutions for banking analytics, enhanced feature engineering readiness, and improved interpretability, while ensuring reproducibility and data provisioning for churn analytics. Strengthened modeling groundwork with robust evaluation, actionable insights, and deployment-oriented conclusions.
Activity
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Quality Metrics
Correctness87.6%
Maintainability87.2%
Architecture81.4%
Performance80.0%
AI Usage22.8%
Skills & Technologies
Programming Languages
CSVJupyter NotebookMarkdownPythonSQL
Technical Skills
Correlation AnalysisData AnalysisData CleaningData EngineeringData ModelingData OrganizationData PreprocessingData ScienceData VisualizationDataset ManagementExploratory Data AnalysisFeature EngineeringFile ManagementGrid SearchGridSearchCV
Repositories Contributed To
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