Clustering Analysis on Synthetic Moons Dataset

This project applies clustering techniques to a synthetic two-moons dataset. It explores data preprocessing with scaling and outlier detection, followed by clustering using K-Means and DBSCAN. The Elbow Method is used to evaluate the optimal number of clusters, while scaling ensures features are normalized for better model performance. This project demonstrates practical steps in unsupervised learning, including visualization and interpretation of clustering results.