Fault Prediction Recommender Model for IoT Enabled Sensors Based Workplace

Industry 5.0 benefits from advancements being made in the field of machine learning and Internet Things. Different sensors have been installed a variety IoT devices present different industries such as transportation, healthcare, manufacturing, agriculture, etc. The these should automatically predict errors due to extensive use urban living. To ensure integrity, precision, security, dependability fidelity sensor nodes, it is, therefore, necessary foresee faults before they occur. Additionally, more data is collected by every day, cloud computing becomes for sustainable proposed model emphasizes solution recommendations that occurred real-life smart mitigate at an early stage, which key requirement today’s offices. monitors real-time health through ML algorithm make efficient increase quality life. Through K-Nearest Neighbor, Decision Tree, Gaussian Naive Bayes Random Forest approach, fault prediction recommender has evaluated shows highest accuracy compared other classifiers. Several performance indicators recall, accuracy, F1 score precision were utilized examine model. results demonstrated effectiveness techniques applied predicting offices with observed best technique maximum 94.27%. In future, deep can also be bigger datasets provide accurate results.