We developed a fullstack web application that uses a custom-engineered ML model, allowing users to predict their proclivity to develop obesity given their background and lifestyle. This project was submitted to SB Hacks VII and was judged top 5 out of over 80 projects. Please visit our Devpost for more information.
Today, over 40% of Americans suffer from obesity. Obesity and excessive weight have been associated with severe risk of illness (CDC). We find this problem especially relevant given the current COVID-19 pandemic, as many people have found themselves confined indoors with little to no exercise. By providing a predictive model to gauge the impact a person's lifestyle has on their health, we hope to aid in preventing health risks that arise from obesity and excessive weight.
This project was created with a wide range of technologies. The frontend was developed using React, Firebase, Axios, and Material-UI. The backend consists of a REST service created with Flask and hosted on Google Cloud Platform. Our ML predictive model was crafted using NumPy, Scikit, and Pandas with a dataset acquired from Kaggle. It employs clustering and the K-Nearest Neighbors algorithm to make inferences from user input.