Diabetes Prediction System Using Machine Learning

This project focuses on developing a predictive model that can identify the likelihood of an individual having diabetes based on medical and personal data. By utilizing machine learning algorithms such as Logistic Regression, Decision Trees, or Random Forest, the system analyzes features like glucose level, blood pressure, age, BMI, and insulin levels. The aim is to support early detection and assist healthcare professionals in making data-driven decisions. The dataset used is typically the PIMA Indian Diabetes dataset, and the system is built using Python and libraries like Scikit-learn, Pandas, and Matplotlib for model training, evaluation, and visualization.