: Human activity recognition requires predicting the action of a person based on sensor-generated data. Due to the enormous number
of applications possible by modern ubiquitous computing devices, it has sparked a lot of attention in recent years. It categorizes data into actions
such as walking, sitting, standing, and lying. The accelerometer and gyroscope were used to generate the sensor data, and the sensor signals
were pre-processed with noise filters. The goal of this research is to anticipate the optimum accuracy for machine learning-based approaches for
Human Activity Recognition. In this study, supervised machine learning methods such as Logistic Regression, SVM, Decision Tree, and
Random Forest were utilized to recognize human behavior using smartphone sensors in a detailed experiment. The proposed machine learningbased technique for accurately predicting human activity involves predicting human actions such as walking, sitting, standing, and lying. When
compared to other supervise classification machine learning techniques, the Random Forest classifier algorithm predicts 97.32 percent
accuracy. While other classifiers, such as Logistic Regression, scored 94.45%, Support Vector Machine 94.55%, and Decision Tree 95.75% also
show good performance. The dataset containing evaluation classification report and confusion matrix to categorize data from priority and the
outcome reveals that the efficacy of the suggested machine learning algorithm technique can be compared to the best accuracy using precision,
recall, and F1 Score.
Keywords: Human activity recognition, Smartphone Sensor, Accelerometer, Gyroscope, Machine learning.