AbdelRayan/AutomaticSleepScoring
Aug 2025 – Aug 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisData VisualizationGraph TheoryJupyter NotebookMachine Learning
unknown
PROFILE
Unknown
Developed a Jupyter Notebook-based Louvain clustering workflow for the AbdelRayan/AutomaticSleepScoring repository, enabling graph-based community detection on state-transition data. The solution constructs a transition matrix from input files, maps states to sleep stages, and establishes data structures for scalable graph analysis. Leveraging Python and graph theory techniques, the workflow enhances interpretability of sleep-scoring transitions and supports reproducible analytics by updating execution counts and clustering results to reflect changes in detected communities and meta-states. This work demonstrates proficiency in data analysis, data visualization, and machine learning, providing a robust foundation for downstream analytics and broader deployment within the project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
492
Activity Months1
Your Network
7 peopleWork History
August 2025
2 Commits • 1 Features
Aug 1, 2025August 2025: Delivered a notebook-based Louvain clustering workflow for state-transition graph analysis in AbdelRayan/AutomaticSleepScoring. The notebook constructs a transition matrix from input data, maps states to stages, and establishes data structures for graph-based community detection, enabling scalable state-space analysis and improved interpretability of sleep-scoring transitions. Execution counts and clustering results were updated to reflect changes in detected communities and meta-states, enhancing reproducibility and downstream analytics.
2 Commits • 1 Features
Aug 1, 2025August 2025: Delivered a notebook-based Louvain clustering workflow for state-transition graph analysis in AbdelRayan/AutomaticSleepScoring. The notebook constructs a transition matrix from input data, maps states to stages, and establishes data structures for graph-based community detection, enabling scalable state-space analysis and improved interpretability of sleep-scoring transitions. Execution counts and clustering results were updated to reflect changes in detected communities and meta-states, enhancing reproducibility and downstream analytics.
August 2025
Activity
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Quality Metrics
Correctness70.0%
Maintainability70.0%
Architecture70.0%
Performance70.0%
AI Usage30.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisData VisualizationGraph TheoryJupyter NotebookMachine Learning
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