  With the advent of Internet of Thing (IoT), and ubiquitous data collected
every moment by either portable (smart phone) or fixed (sensor) devices, it is
important to gain insights and meaningful information from the sensor data in
context-aware computing environments. Many researches have been implemented by
scientists in different fields, to analyze such data for the purpose of
security, energy efficiency, building reliability and smart environments. One
study, that many researchers are interested in, is to utilize Machine Learning
techniques for occupancy detection where the aforementioned sensors gather
information about the environment. This paper provides a solution to detect
occupancy using sensor data by using and testing several variables.
Additionally we show the analysis performed over the gathered data using
Machine Learning and pattern recognition mechanisms is possible to determine
the occupancy of indoor environments. Seven famous algorithms in Machine
Learning, namely as Decision Tree, Random Forest, Gradient Boosting Machine,
Logistic Regression, Naive Bayes, Kernelized SVM and K-Nearest Neighbors are
tested and compared in this study.
