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
Over two months, this developer contributed to SpikyCherry/DSA3101_group9 by building marketing analytics notebooks and a campaign ROI prediction model for banking data. They implemented data loading, cleaning, and feature engineering to support ROI analysis, then developed and evaluated machine learning models—including Ridge Regression and Random Forest—using Python and Scikit-learn. Their work included scalable data ingestion mechanisms for bulk uploads, enabling analytics workflows on large datasets. They also improved documentation for clarity and maintained code hygiene. The developer’s contributions demonstrated depth in data preprocessing, model evaluation, and visualization, resulting in deployment-ready artifacts that support data-driven business decisions.
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
72 people
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
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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
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