jdpipping/summer-lab
Jun 2025 – Jul 2025
2 Months active
Languages Used
RStan
Technical Skills
Bayesian InferenceClusteringData AnalysisData VisualizationExploratory Data AnalysisMachine Learning
Maximilian J. Gebauer
PROFILE
Maximilian J. Gebauer
During two months on the jdpipping/summer-lab repository, Gebauer developed a suite of analytics and recommendation tools spanning sports and music domains. He built modular R Markdown notebooks for NBA, NFL, baseball, and golf analytics, applying Bayesian inference, XGBoost, and multinomial logistic regression to model team performance, win probabilities, and player outcomes. Gebauer also designed a reproducible Spotify song recommendation pipeline using ensemble methods and data preprocessing in R, enabling personalized user predictions. His work emphasized reproducibility, clear documentation, and stakeholder alignment, demonstrating depth in statistical modeling, machine learning, and data visualization while delivering production-ready, business-aligned analytical frameworks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
6
Lines of code
5,645
Activity Months2
Your Network
38 people
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for jdpipping/summer-lab focusing on key accomplishments, business value, and technical execution. Delivered a foundational, reproducible Spotify song recommendation model framework and associated documentation to enable rapid iteration and stakeholder alignment for personalized user experiences.
1 Commits • 1 Features
Jul 1, 2025July 2025 monthly summary for jdpipping/summer-lab focusing on key accomplishments, business value, and technical execution. Delivered a foundational, reproducible Spotify song recommendation model framework and associated documentation to enable rapid iteration and stakeholder alignment for personalized user experiences.
July 2025
June 2025
16 Commits • 5 Features
Jun 1, 2025June 2025 monthly summary for jdpipping/summer-lab: Delivered multi-sport analytics notebook suites (NBA, NFL, Baseball, Golf) and Educational Data Science notebooks, enabling data-driven decision making and teaching resources. Implemented advanced modeling approaches (XGBoost, Bayesian modeling, multinomial logistic regression, MLE/Empirical Bayes) with RMSE evaluation, and enhanced reproducibility and documentation across the repository.
June 2025
16 Commits • 5 Features
Jun 1, 2025June 2025 monthly summary for jdpipping/summer-lab: Delivered multi-sport analytics notebook suites (NBA, NFL, Baseball, Golf) and Educational Data Science notebooks, enabling data-driven decision making and teaching resources. Implemented advanced modeling approaches (XGBoost, Bayesian modeling, multinomial logistic regression, MLE/Empirical Bayes) with RMSE evaluation, and enhanced reproducibility and documentation across the repository.
Activity
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Quality Metrics
Correctness83.4%
Maintainability80.0%
Architecture81.2%
Performance69.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
RStan
Technical Skills
Bayesian InferenceClusteringData AnalysisData PreprocessingData VisualizationEnsemble MethodsExploratory Data AnalysisMachine LearningModel TrainingR MarkdownR ProgrammingSimulationStatistical ModelingXGBoost
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