GlucoPredict: Leveraging Logistic Regression to Predict Diabetes Risk

GlucoPredict is a machine learning-based project designed to predict the risk of diabetes using logistic regression, a powerful statistical method. The goal of this project is to develop a model that can accurately classify individuals as either at risk of diabetes or not, based on a set of medical and lifestyle-related features. The dataset used for training and evaluation consists of key factors such as age, BMI, blood pressure, glucose levels, and family history of diabetes. By applying logistic regression, the model can identify complex patterns and relationships within these variables, providing a clear, probabilistic prediction of diabetes risk. This approach offers a transparent and interpretable way to understand how individual factors contribute to the likelihood of developing diabetes. The project aims to address the growing global concern of diabetes by providing an accessible, data-driven tool for early detection. Early diagnosis is crucial in managing and preventing complications associated with diabetes, and this model offers healthcare professionals a powerful resource for decision-making. By leveraging logistic regression, GlucoPredict not only delivers high accuracy but also ensures that the predictions are interpretable, enabling users to understand the underlying factors influencing the outcomes. This project demonstrates the potential of machine learning in improving healthcare outcomes