SpikyCherry/DSA3101_group9
Mar 2025 – Apr 2025
2 Months active
Languages Used
Jupyter NotebookPythonShell
Technical Skills
Data AnalysisData CleaningData VisualizationExploratory Data Analysis (EDA)Feature EngineeringMachine Learning
Xyf9866
PROFILE
Xyf9866
Contributed to SpikyCherry/DSA3101_group9 by developing marketing analytics notebooks focused on exploratory data analysis and ROI measurement for banking campaigns. Built data loading, cleaning, and feature engineering pipelines in Python and Pandas, incorporating cost, CLV, CAC, and ROI features to support business-driven insights. Designed and trained predictive models, comparing approaches such as Ridge Regression and Random Forest using Scikit-learn, and selected models based on interpretability and performance. Delivered scalable data ingestion mechanisms for bulk uploads, enabling analytics workflows on large datasets. Enhanced documentation for clarity and maintained code hygiene, ensuring deployment-ready artifacts and actionable visualizations for business decision-making.
Overall Statistics
Feature vs Bugs
60%Features
Repository Contributions
7Total
Bugs
2
Commits
7
Features
3
Lines of code
11,079
Activity Months2
Your Network
78 peopleShared Repositories
8
alvislowMember
syyyyyqqqqqqqMember
gggwwwjjjMember
1234tjjMember
guo wenjinMember
Hu CanMember
SpikyCherryMember
Yuqing ShenMember
Work History
April 2025
5 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for SpikyCherry/DSA3101_group9: Delivered data-driven ROI analytics capability and scalable data ingestion, with deployment-ready artifacts and improved documentation; solid business value from predictive analytics and scalable intake.
5 Commits • 2 Features
Apr 1, 2025April 2025 monthly summary for SpikyCherry/DSA3101_group9: Delivered data-driven ROI analytics capability and scalable data ingestion, with deployment-ready artifacts and improved documentation; solid business value from predictive analytics and scalable intake.
April 2025
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly work summary focusing on key accomplishments in SpikyCherry/DSA3101_group9. Delivered Marketing Analytics Notebooks for Banking Marketing (EDA and ROI measurement) with data loading, cleaning, initial feature engineering, and ROI-related features. Initiated baseline model training and established analytics foundation for ROI analysis. No major bugs reported; commits documented.
March 2025
2 Commits • 1 Features
Mar 1, 2025March 2025 monthly work summary focusing on key accomplishments in SpikyCherry/DSA3101_group9. Delivered Marketing Analytics Notebooks for Banking Marketing (EDA and ROI measurement) with data loading, cleaning, initial feature engineering, and ROI-related features. Initiated baseline model training and established analytics foundation for ROI analysis. No major bugs reported; commits documented.
Activity
Loading activity data...
Quality Metrics
Correctness77.2%
Maintainability71.4%
Architecture68.6%
Performance71.4%
AI Usage22.8%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonShell
Technical Skills
Data AnalysisData CleaningData PreprocessingData VisualizationExploratory Data Analysis (EDA)Feature EngineeringFeature ImportanceHyperparameter TuningMachine LearningMatplotlibModel EvaluationPandasPythonRandom ForestRidge Regression
Repositories Contributed To
Overview of all repositories you've contributed to across your timeline