Makine öğrenmeyi kullanarak IoT sistemi hata testi (lıneer regresyon)

In the age of the Internet of Things (IoT), day-to-day objects are outfitted with sensors and actuators. It is also referred to as IoT units that gather information and function moves through speaking wi.th every other. Electrical units with constrained sources may additionally be subjected to big stresses or exterior influences that may additionally lead to their failure. In provider environments security is a imperative requirement, failure ought to be averted earlier than it motives any damage. By imposing predictive upkeep (PdM) the use of laptop gaining knowledge of (ML), computer getting to know algorithms can be utilized to predict screw ups and grant ample renovation time. IoT gadgets gather statistics that can be used to educate desktop gaining knowledge of algorithms to apprehend failure patterns of character devices. This thesis introduces (Decision Tree, Logistic Regression, k-nearest neighbors, Random Forest, and Gradient Boosting) algorithms and its addendum to predict the failure of electrical gadgets by means of the usage of Internet of Things devices.