Integration of FastAPI-Based Machine Learning Model with Android Application for Real-Time Calories Burnt Prediction

Abstract

Neha Bansal and Bhawna Singla

This paper presents the design and implementation of a system integrating a FastAPI backend serving a machine learning model for predicting calories burnt with a native Android application. The backend uses a Random Forest Regressor trained on health and exercise data to deliver accurate calorie estimations. The Android application interacts with the API to send user input and receive real-time predictions. The system demonstrates seamless communication between Python-based APIs and mobile platforms, facilitating personalized fitness monitoring on portable devices.

PDF