• Harpreet Kaur Lohia
• Simran Kaur Dari
• Roshni Murli Singh
• Shravanti Shankar Kalwal
• Prof. Anand.D.G. Donald

Activity recognition is one of the leading applications of machine learning algorithms nowadays. It is being used in the field of biomedical engineering, game development, developing better stats for sports training etc. Data from the sensors attached to a person can be utilised to train supervised machine learning models in order to predict the activity being carried out by the person. In this paper we will be using Data available at UCI machine learning Repository. It contains data generated from accelerometer, gyroscope and other sensors of Smartphone to train supervised predictive models using machine learning techniques like SVM, Random Forest and decision tree to generate a model. which can be used to predict the kind of movement being carried out by the person which is divided into six categories walking, walking upstairs, walking downstairs, sitting, standing and laying. Keywords-

Human Activity Recognition, Random Forest, SVM, classifier, Logistic Regression

"Human Activity Recognition using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.f619-f622, June-2022, Available :http://www.jetir.org/papers/JETIR2206579.pdf

"Human Activity Recognition using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppf619-f622, June-2022, Available at : http://www.jetir.org/papers/JETIR2206579.pdf