Cluster Analysis : Unsupervised Machine Learning in Python

1. Describe the input and output of a clustering model
2. Prepare data with feature engineering techniques
3. Implement K-Means Clustering, Hierarchical Clustering, Mean Shift Clustering, DBSCAN, OPTICS and Spectral Clustering models
4. Determine the optimal number of clusters
5. Use a variety of performance metrics such as Silhouette Score, Calinski-Harabasz Index and Davies-Bouldin Index.