Anamoly Detection Supercomputer Logs

The aim of the whole project was to detect anomalies in the supercomputer logs based on certain events and messages, extracting critical features using feature engineering and running Random Forest Machine Learning model to evaluate the performance. Achieved overall test accuracy to around 99% using all the classification criteria - confusion matrix, precision, recall etc

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
README.md		README.md
bgl.csv		bgl.csv
bgl_anomaly_file1.ipynb		bgl_anomaly_file1.ipynb
bgl_anomaly_file2.ipynb		bgl_anomaly_file2.ipynb
project_report_bgl_anomaly.pdf		project_report_bgl_anomaly.pdf
video_presentation_bgl_anomaly.mp4		video_presentation_bgl_anomaly.mp4

Provide feedback