Predicting Diabetes Onset Using Logistic Regression in R

This project focuses on predicting the onset of diabetes using logistic regression on
the Pima Indians Diabetes Database from Kaggle. The dataset includes variables such as glucose
concentration, blood pressure, BMI, and age. The model achieved an AUC value of 0.8396 and
an overall accuracy of 78.39%, effectively identifying key risk factors associated with diabetes.

Tools/Skills

R, Statistical Modeling, Machine Learning, Logistic Regression