SCRN92/Classification-of-Human-Activities-Using-Smartphone-Sensor-Data

This project aims to develop a machine learning model for accurately classifying human activities using smartphone sensor data.

Classification-of-Human-Activities-Using-Smartphone-Sensor-Data

This project aims to develop a machine learning model for accurately classifying human activities using smartphone sensor data. The dataset consists of recordings from individuals performing various activities of daily living, captured through wearable smartphones. The objective is to categorize activities such as walking, sitting, standing, and more. The project applies data preprocessing, exploratory data analysis, and feature engineering techniques. Several supervised learning algorithms are implemented to train the model, enabling it to accurately classify human activities. The successful classification of activities has significant implications in industries such as fitness tracking and healthcare, where it can be used for behavior monitoring and remote patient care. The project contributes to the field of human activity recognition and provides a foundation for further research and advancements in this domain.