Human Activity Recognition has a long history of research and requires further exploration to produce effective and optimal outcomes. Areas such as medicine, daily routine, and security are some of the benefits that smartphone enables via embedded sensors. Our work has chosen sensor data of six activities such as standing, walking, laying from pre-recorded dataset gathered via smartphone to evaluate the performance of various supervised machine learning algorithms. The results suggest that the logistic regression has been an optimal choice based on experiments. Whereas, Support Vector Machine (SVM) has shown to perform well with 95% accuracy.