Recognition of human activities has become a very popular problem that has been widely studied with the development of sensors embedded in mobile devices and increasingly widespread methods of machine learning. For the solution of this problem, the sensor data of the different movements collected are labeled with the movements performed and turned into a classification problem. Different human activities are tried to be distinguished by applying Gradient Boosting, Random Forest, AdaBoost, Gaussian Naive Bayes classification algorithms on the collected data. Performance examinations and accuracy values are evaluated with the combination confusion matrix. It is observed that Gradient Boosting showed the best performance overall analysis. Human activity recognition is used in health practices, calculating personal daily calories, analyzing the health status, monitoring the movements performed by the elderly people in their environment, human position tracking, and various security applications

Keywords : Human activity recognition; Sensor; Gradient Boosting; Random Forest; AdaBoost; Gaussian Naive Bayes.