Diabetes Risk Analysis with PCA & K-Means Clustering

This analysis explores diabetes risk using data science methods, starting with understanding its significance and dataset features. Key techniques include PCA for dimensionality reduction and K-Means clustering for pattern identification. The findings emphasise clustering's role in risk insights, supported by visualisation. Future steps focus on predictive modelling, real-world applications, and leveraging more data for improved outcomes.