This repository contains a machine learning project to detect diabetes in individuals based on key health indicators. The model uses various classification algorithms, including Logistic Regression, Random Forest, and Gradient Boosting.
Early detection of diabetes can significantly improve patient outcomes and quality of life. This project aims to develop a machine learning model to predict the likelihood of diabetes in individuals based on their health indicators such as glucose level, blood pressure, BMI, and more.
The dataset used for this project is the Pima Indians Diabetes Database. It contains 8 features and 1 target variable indicating whether an individual has diabetes or not.
The performance of the models is evaluated using various metrics such as accuracy, precision, recall, and F1-score. Confusion matrices, ROC curves, and precision-recall curves are also plotted to provide a comprehensive evaluation.