{"id":24841491,"url":"https://github.com/sadratehranian/pem-fuel-cell","last_synced_at":"2025-03-26T05:42:05.574Z","repository":{"id":273423427,"uuid":"919668678","full_name":"Sadratehranian/PEM-Fuel-Cell","owner":"Sadratehranian","description":"The methodology section details the use of Python for data processing and analysis, employing statistical and machine learning-based anomaly detection techniques to identify potential issues in fuel cell stacks. 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The goal is to identify patterns, detect anomalies, and visualize results for improved analysis.\n\n## Contents\n\n- **`Main.py`**: Python script for loading, cleaning, and analyzing the dataset.\n- **`data3040b.xlsx`**: Dataset containing voltage readings for multiple cells.\n- **`README.md`**: Documentation for this project.\n\n## Features\n\n1. Data Cleaning:\n   - Removes redundant rows (e.g., empty rows or units).\n   - Ensures proper formatting for analysis.\n\n2. Feature Engineering:\n   - Calculates rolling statistics (mean, standard deviation, etc.).\n   - Detects anomalies in cell voltage readings.\n\n3. Anomaly Detection Techniques:\n   - **Isolation Forest**\n   - **DBSCAN**\n   - **K-Means Clustering**\n   - **One-Class SVM**\n\n4. Visualization:\n   - Distribution histograms\n   - Time-series plots\n   - Scatter plots for relationships\n   - Heatmaps for correlation\n\n## Setup and Installation\n\n1. Clone this repository:\n   ```bash\n   git clone https://github.com/yourusername/Data3040_Analysis.git\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsadratehranian%2Fpem-fuel-cell","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsadratehranian%2Fpem-fuel-cell","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsadratehranian%2Fpem-fuel-cell/lists"}