Diabetes Prediction Using Machine Learning

Diabetes is one of the chronic diseases that causes blood sugar levels to rise. If diabetes is left untreated and undiagnosed, it can lead to complications. The time-consuming identification process leads to a patient's referral to a diagnostic Centre and consultation with a doctor. Predictive analytics in healthcare is a difficult challenge, but it can eventually assist physicians in making timely decisions about a patient's health and condition based on data. The emergence of machine learning methods solves this crucial issue. The aim of this project is to create a model that can reliably predict the accuracy of diabetes in patients. Dataset splits into three then classification techniques are implemented. Diabetes prediction dataset is divided into Training dataset and testing dataset that is used to fit the model. We will use SMOTE analysis for accurate prediction of model on unseen dataset.