End-to-end ML modelling process for clustering problem
  1. Data reading and preprocessing: missing data, visualization, scaling
  2. Algorithm selection: K-Means, Hierarchical
  3. Optimal cluster selection: Elbow method, Dendrogram
 
Algorithms
  1. K-Means Clustering
  2. Hierarchical Clustering

Examples
  1. ISLR college data (2 clusters)
  2. kaggle social network ads (2 clusters)
  3. sklearn make_blobs data (4 clusters)
  4. kaggle mall customers data (5 clusters)