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This project aims to analyze diabetes data using data management, captivating visualizations, and cutting-edge machine learning techniques to predict the presence of diabetes in individuals. Our robust dataset includes comprehensive health exam results and family history.
This project is using machine learning to predict the likelihood of a person having diabetes. The dataset used in this project is the "diabetes.csv" file, which contains information on various health factors such as glucose levels, blood pressure, BMI, and age, among others. The goal is to use this data to train a machine learning model, specifical
The Major goal of this study was to create a web-based interface to run inference on a machine learning model. The Web application developed with the flask framework helps to predict if a person has Diabetes mellitus or not via a web-based form.
This repository contains the code and resources for a machine learning project aimed at diabetes detection. We have implemented multiple machine learning models and data visualization techniques to build an accurate diabetes detection system.
This repository contains script and DUK files for Ethan de Villiers' research on Classification Models under Beverley Shields and Angus Jones at University of Exeter, Diabetes Team.