Room Occupancy Detection Using IoT Sensor Data and Machine Learning
Abstract
We examine room occupancy detection utilizing IoT sensor data, leveraging characteristic parameters such as temperature, mugginess, light, and CO2 levels. Our approach uses machine learning models, counting Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM) to anticipate room occupancy status based on these sensor readings. Our models can anticipate occupancy, with the Random Forest model finishing the preeminent essential execution, boasting an accuracy of 99.35%. Our commitment lies in showing the adequacy of combining IoT sensor data with progressed machine learning methodologies to upgrade room occupancy detection, publicizing crucial potential for applications in brilliantly building organizations to optimize imperativeness utilization and make strides in inhabitant consolation.
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