This project demonstrates a comprehensive analysis of a diabetes-related dataset using various data science techniques in R. The analysis includes Principal Component Analysis (PCA) for dimensionality reduction and visualization, followed by the implementation of classic Machine Learning algorithms like K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees for classification tasks. The objective is to explore the relationships between variables, reduce data dimensionality, and build predictive models to understand potential patterns in the dataset.