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
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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. It turned out to be a99% accurate on test set using all the classification criteria - confusion m…
navdeep94/Anamoly_Detection_Supercomputer_Logs_Machine_Learning
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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. It turned out to be a99% accurate on test set using all the classification criteria - confusion m…
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