blackSwanCS/Higgs_collaboration_B
Jun 2025 – Jun 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Boosted Decision TreesData AnalysisData CleaningData PreprocessingData ScienceFile Management
Ayadi Mahdi
PROFILE
Ayadi Mahdi
Mahdi Ayadi developed an end-to-end Boosted Decision Tree workflow for the blackSwanCS/Higgs_collaboration_B repository, focusing on automating model training, persistence, and evaluation using Python and XGBoost. He integrated hyperparameter optimization and versioning, enabling reproducible experiments and streamlined model management within Jupyter Notebooks. Mahdi addressed a model integration bug, ensuring the correct BDT model was used throughout the notebook workflow, and improved overall reproducibility by cleaning up notebook outputs and updating configuration files. His work demonstrated depth in data preprocessing, model persistence, and file management, resulting in a robust, maintainable pipeline for machine learning experimentation and evaluation.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
16Total
Bugs
1
Commits
16
Features
2
Lines of code
19,469
Activity Months1
Your Network
41 people
Work History
June 2025
16 Commits • 2 Features
Jun 1, 2025June 2025 performance summary for blackSwanCS/Higgs_collaboration_B. Focused on delivering an end-to-end Boosted Decision Tree (BDT) workflow, stabilizing notebook integration, and enhancing reproducibility for experiment workflows. Delivered an automated end-to-end BDT training, persistence, and evaluation pipeline using XGBoost, including save/load, model/scaler persistence, evaluation metrics (AUC and significance), and integration of hyperparameter optimization and versioning for experiments. Implemented a bug fix to BDT model integration, ensuring the corrected model is used within the notebook workflow. Performed notebook cleanup and configuration improvements to reduce noise and improve reproducibility. All changes tracked via commits across boosted_decision_tree.py, model.py, and notebook code.
16 Commits • 2 Features
Jun 1, 2025June 2025 performance summary for blackSwanCS/Higgs_collaboration_B. Focused on delivering an end-to-end Boosted Decision Tree (BDT) workflow, stabilizing notebook integration, and enhancing reproducibility for experiment workflows. Delivered an automated end-to-end BDT training, persistence, and evaluation pipeline using XGBoost, including save/load, model/scaler persistence, evaluation metrics (AUC and significance), and integration of hyperparameter optimization and versioning for experiments. Implemented a bug fix to BDT model integration, ensuring the corrected model is used within the notebook workflow. Performed notebook cleanup and configuration improvements to reduce noise and improve reproducibility. All changes tracked via commits across boosted_decision_tree.py, model.py, and notebook code.
June 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability90.0%
Architecture87.6%
Performance83.8%
AI Usage22.6%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Boosted Decision TreesData AnalysisData CleaningData PreprocessingData ScienceFile ManagementHyperparameter OptimizationJupyter NotebookMachine LearningModel PersistencePythonPython DevelopmentScikit-learnXGBoost
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