README.md

---

title: Clustering
duration: "3:00"
creator:
    name: Marc Harper
    city: LA

---

Clustering

DS | Lesson 11

LEARNING OBJECTIVES

After this lesson, you will be able to:

• Understand unsupervised versus supervised learning

• Apply k-Means and DBSCAN clustering

• Combine clustering with classification and regression

STUDENT PRE-WORK

Before this lesson, you should already be able to:

• Use sckit-learn to fit models

• Use seaborn to create plots

• Use pandas to load datasets

Instructors: This is a flex lesson and an example lesson on clustering that you are free to use. You will be switching back and forth between the slides and the notebooks. Approximate timing:

• One hour for k-Means and exercises

• One hour for DBSCAN and exercises

• One hour on building models after clustering and silhouette metric

Be sure to copy the slide deck.

BEFORE NEXT CLASS

UPCOMING PROJECTS	Final Project, Deliverable 2