Prediction of Hard Drive Failure using Machine Learning

Authors

  • Elizabeth Atekoja

DOI:

https://doi.org/10.34257/GJCSTDVOL24IS1PG41

Keywords:

Abstract

The reliability of hard drives is paramount for maintaining data integrity and availability in cloud services and enterprise-level data centres where unexpected failures significantly impact operational efficiency and general performance This work aims to develop a predictive model using regression analysis to accurately forecast imminent hard drive failures based on historical operational data specifically SMART Self-Monitoring Analysis and Reporting Technology attributes The study evaluated various regression models which comprises Decision Tree Random Forest Support Vector Machine SVM Gradient Boosting and Neural Network The outcomes indicated that the Random Forest model with an MSE of 24 7427 and an R2 of 0 9876 and the Neural Network model with an MSE of 22 6011 and an R2 of 0 7442 as the best performing models as they demonstrated high predictive accuracy and robustness In contrast the SVM model showed poor performance with an MSE of 2888 8623 and a negative R2 of - 0 4465

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How to Cite

Elizabeth Atekoja. (2024). Prediction of Hard Drive Failure using Machine Learning. Global Journal of Computer Science and Technology, 24(D1), 43–56. https://doi.org/10.34257/GJCSTDVOL24IS1PG41

Prediction of Hard Drive Failure using Machine Learning

Published

2024-08-28