*S11-T01:clustering algorithms.

Learn how to use Python to performance unsupervised learning classification

Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. Here we are going to learn how to cluster, transform, visualize, and extract insights from unlabeled datasets.

• Become familiar and learn about clustering algorithms.

• Perform cyclic feature encoding (converting the original time values to their corresponding cosine and sine value).

• Does the cluster algorithms capture the effect of encoding cyclic characteristics?

Learning Objectives:

• K Means
• Hierarchical clustering

To run this notebook you should will need to have previously downloaded the Kaggle API credentials.

Also, furthermore, to reproduce the notebook it will be necessary to download the my_func folder.

To see the notebook in the browser, click here