Exercise

# DBSCAN

In this exercise you're going to explore using a **density based clustering** method (DBSCAN) to detect fraud. The advantage of DBSCAN is that you **do not need to define the number of clusters** beforehand. Also, DBSCAN can handle weirdly shaped data (i.e. non-convex) much better than K-means can. This time, you are not going to take the outliers of the clusters and use that for fraud, but take the **smallest clusters** in the data and label those as fraud. You again have the scaled dataset, i.e. `X_scaled`

available. Let's give it a try!

Instructions

**100 XP**

- Import
`DBSCAN`

. - Initialize a DBSCAN model setting the maximum distance between two samples to 0.9 and the minimum observations in the clusters to 10, and fit the model to the scaled data.
- Obtain the predicted labels, these are the cluster numbers assigned to an observation.
- Print the number of clusters and the rest of the performance metrics.