jdpipping/summer-lab
Jun 2025 – Jul 2025
2 Months active
Languages Used
RSQL
Technical Skills
BootstrappingConfidence IntervalsData AnalysisData VisualizationLinear RegressionMachine Learning
abalajee
PROFILE
Abalajee
Developed a cross-sport analytics suite in the jdpipping/summer-lab repository, delivering reproducible R-based workflows for basketball, football, and baseball data analysis. Built 13 modular scripts supporting data loading, visualization, and statistical modeling, including regression, random forests, and confidence interval comparisons. Extended the suite with an end-to-end Spotify user-song prediction pipeline, implementing data preprocessing, genre clustering, feature engineering, and XGBoost model training. Focused on scalable, educational tooling for dashboards and teaching materials, the work emphasized reproducibility and clear data flows. Leveraged R and SQL for statistical analysis, machine learning, and data visualization, with all features delivered and no major bugs reported.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
14Total
Bugs
0
Commits
14
Features
5
Lines of code
2,350
Activity Months2
Your Network
52 peopleShared Repositories
20
aiwenli123Member
Arnab ChaudhuriMember
bucknerm28Member
Dawei SunMember
dbukasaMember
Tianshu FengMember
Maximilian J. GebauerMember
Henry GaudetMember
Henry HuangMember
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) monthly summary for jdpipping/summer-lab: Delivered an end-to-end Spotify User-Added Song Prediction Competition Pipeline in R, including data preparation, genre clustering, feature engineering, and model training/evaluation with XGBoost. The pipeline is implemented in rita_competition.R. This work establishes a reproducible workflow for rapid experimentation and benchmarking on Spotify-derived features, enabling data-driven insights into user-song interactions. No major bugs fixed this month.
1 Commits • 1 Features
Jul 1, 2025July 2025 (2025-07) monthly summary for jdpipping/summer-lab: Delivered an end-to-end Spotify User-Added Song Prediction Competition Pipeline in R, including data preparation, genre clustering, feature engineering, and model training/evaluation with XGBoost. The pipeline is implemented in rita_competition.R. This work establishes a reproducible workflow for rapid experimentation and benchmarking on Spotify-derived features, enabling data-driven insights into user-song interactions. No major bugs fixed this month.
July 2025
June 2025
13 Commits • 4 Features
Jun 1, 2025June 2025 performance summary for the jdpipping/summer-lab repository: Delivered a cross-sport analytics suite (Rita Labs) with robust data loading, visualization, and statistical modeling capabilities across basketball, football, and baseball. Implemented 13 R scripts spanning four analytics domains, establishing a scalable, reproducible analytics foundation. Key work spanned park effects on baseball runs, basketball analytics, and football scoring and win-probability modeling, enabling data-driven insights for educators and analysts. No major bugs reported; focus was on feature delivery and tooling enhancements to support dashboards and teaching materials. Technologies demonstrated include R-based data analysis, regression modeling (logistic, spline), random forests, and data visualization.
June 2025
13 Commits • 4 Features
Jun 1, 2025June 2025 performance summary for the jdpipping/summer-lab repository: Delivered a cross-sport analytics suite (Rita Labs) with robust data loading, visualization, and statistical modeling capabilities across basketball, football, and baseball. Implemented 13 R scripts spanning four analytics domains, establishing a scalable, reproducible analytics foundation. Key work spanned park effects on baseball runs, basketball analytics, and football scoring and win-probability modeling, enabling data-driven insights for educators and analysts. No major bugs reported; focus was on feature delivery and tooling enhancements to support dashboards and teaching materials. Technologies demonstrated include R-based data analysis, regression modeling (logistic, spline), random forests, and data visualization.
Activity
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Quality Metrics
Correctness83.6%
Maintainability80.0%
Architecture80.0%
Performance72.8%
AI Usage24.2%
Skills & Technologies
Programming Languages
RSQL
Technical Skills
BootstrappingClusteringConfidence IntervalsData AnalysisData PreprocessingData VisualizationLinear RegressionMachine LearningPermutation TestsR ProgrammingSimulationStatistical AnalysisStatistical ModelingXGBoost
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