Optimizing cholesterol data using a shallow neural network

This project uses data from 252 patients, including 13 anatomical measurements and body fat percentage, to train a neural network for predic

While the dataset itself may be small, this project demonstrates the effective application of shallow neural networks for predictive modeling in a healthcare context. By leveraging a dataset of 252 patients with 13 anatomical measurements and corresponding body fat percentages, this work showcases the ability of a shallow neural network to learn the complex relationship between these variables and accurately predict body fat percentage in new patients. This approach highlights the potential of shallow neural networks as efficient and accurate tools for body fat prediction, offering a valuable contribution to the field of health and wellness.