<![CDATA[This study explores the application of machine learning (ML) techniques in detecting hardware failures in Local Area Networks (LANs). As networks become increasingly complex, the ability to predict and address hardware issues before they lead to system failures is crucial for maintaining network reliability and performance. The research investigates several machine learning algorithms, including supervised and unsupervised models, to analyze network data and identify early signs of potential hardware malfunctions. The study emphasizes the use of features such as network traffic patterns, hardware performance metrics, and error logs to train models capable of detecting anomalies and predicting failures. The effectiveness of these models is evaluated based on their accuracy, precision, and recall in identifying hardware failures. The findings aim to contribute to the development of more efficient and proactive failure detection systems that can enhance network uptime and reduce the costs associated with unexpected hardware downtimes.]]>
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