blackSwanCS/Higgs_collaboration_B
Jun 2025 – Jun 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData ScienceData VisualizationHyperparameter OptimizationJupyter NotebookJupyter Notebooks
Yasmine
PROFILE
Yasmine
Yasmine Hakmouni enhanced the blackSwanCS/Higgs_collaboration_B repository by delivering a comprehensive upgrade to the HiggsML Jupyter Notebook, focusing on end-to-end machine learning workflow improvements. She implemented robust data loading and exploration features, integrated advanced data visualization, and developed a hyperparameter optimization workflow for XGBoost that included a significance-curve plot to guide model tuning. Yasmine also introduced a flexible model_type parameter, enabling users to compare XGBoost, Scikit-Learn GradientBoosting, and LightGBM models within a unified pipeline. Her work demonstrated depth in Python, data science, and machine learning, providing a more efficient and extensible experimentation environment for users.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
5,849
Activity Months1
Your Network
41 people
Work History
June 2025
4 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for blackSwanCS/Higgs_collaboration_B: Delivered a major upgrade to the HiggsML notebook enabling an end-to-end ML workflow with enhanced data loading and exploration, richer visualizations, and robust hyperparameter optimization (HPO) for XGBoost including a significance-curve plot. Introduced flexible multi-model support via a model_type parameter to enable side-by-side comparisons among XGBoost, Scikit-Learn GradientBoosting, and LightGBM within the same pipeline. This work is anchored by a set of commits that demonstrate the progression from HPO setup to a full multi-model pipeline: 8f6121660ca991bf386c17ea023cd17328182fbf; 406e60e725dcb15a2645a51f50799914c0f2bb0b; 1cb104a10d6e7bbed0e97b9d8f83a9695031cc45; 1955bed2028f23da3d36c9bab0cb73f5785b6647.
4 Commits • 1 Features
Jun 1, 2025June 2025 performance summary for blackSwanCS/Higgs_collaboration_B: Delivered a major upgrade to the HiggsML notebook enabling an end-to-end ML workflow with enhanced data loading and exploration, richer visualizations, and robust hyperparameter optimization (HPO) for XGBoost including a significance-curve plot. Introduced flexible multi-model support via a model_type parameter to enable side-by-side comparisons among XGBoost, Scikit-Learn GradientBoosting, and LightGBM within the same pipeline. This work is anchored by a set of commits that demonstrate the progression from HPO setup to a full multi-model pipeline: 8f6121660ca991bf386c17ea023cd17328182fbf; 406e60e725dcb15a2645a51f50799914c0f2bb0b; 1cb104a10d6e7bbed0e97b9d8f83a9695031cc45; 1955bed2028f23da3d36c9bab0cb73f5785b6647.
June 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture75.0%
Performance65.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData ScienceData VisualizationHyperparameter OptimizationJupyter NotebookJupyter NotebooksMachine LearningPythonXGBoost
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