This paper predicts fitness application data of people using two machine learning techniques, linear regression and decision trees. Fitness Tracker collects data pertaining of physical activities such as steps, distance, calories burnt, sleep routine, etc. This paper explores the correlation between the aforementioned physical activities to find out which of the following affects calories burnt the highest. Comparison is done among two popular machine learning algorithms to depict their performance, interpretability, scalability, and applicability to the different datasets. This allows for us to maximize efficiency by reducing the collection of unnecessary data and further discuss suitable machine learning algorithms to implement in fitness devices for better accuracy in readings from fitness applications.