AbdelRayan/AutomaticSleepScoring
Aug 2025 – Aug 2025
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
ClusteringData AnalysisDimensionality ReductionEMDJupyter NotebookMachine Learning
SYasheel
PROFILE
Syasheel
Developed a Colab-friendly project scaffold and three Jupyter notebooks for the AbdelRayan/AutomaticSleepScoring repository, enabling reproducible sleep-scoring experiments and electrophysiology data analysis. The work established a scalable, notebook-driven workflow using Python, Pandas, and Scikit-learn, covering the full pipeline from data loading to clustering and frequency-domain preprocessing. Delivered a clustering notebook implementing K-Means and PCA with visualizations for PFC and HPC, and an EMD mask-sifting notebook for electrophysiology data, including preprocessing and instantaneous frequency extraction. Focus remained on feature delivery, reproducibility, and maintainability, with no major bugs reported during the period and all work tracked through versioned commits.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
3
Lines of code
5,128
Activity Months1
Your Network
8 peopleSame Organization
@iiserb.ac.in1
Work History
August 2025
4 Commits • 3 Features
Aug 1, 2025August 2025 monthly summary for AbdelRayan/AutomaticSleepScoring: Delivered a Colab-friendly project scaffold and three notebooks to accelerate reproducible sleep-scoring experiments and electrophysiology data analysis. This work establishes a scalable, notebook-driven workflow and demonstrates end-to-end data analysis from loading to clustering and frequency-domain preprocessing. No major bugs reported this month; focus remained on feature delivery, reproducibility, and maintainability.
4 Commits • 3 Features
Aug 1, 2025August 2025 monthly summary for AbdelRayan/AutomaticSleepScoring: Delivered a Colab-friendly project scaffold and three notebooks to accelerate reproducible sleep-scoring experiments and electrophysiology data analysis. This work establishes a scalable, notebook-driven workflow and demonstrates end-to-end data analysis from loading to clustering and frequency-domain preprocessing. No major bugs reported this month; focus remained on feature delivery, reproducibility, and maintainability.
August 2025
Activity
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Quality Metrics
Correctness85.0%
Maintainability85.0%
Architecture85.0%
Performance80.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
ClusteringData AnalysisDimensionality ReductionEMDJupyter NotebookMachine LearningMatplotlibPandasPythonScikit-learnSeabornSignal Processing
Repositories Contributed To
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