Unsupervised learning

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Hadrien Lacroix

Content Developer at DataCamp

Unsupervised learning

Unsupervised learning

• Unsupervised learning = no target column
  • No guidance
  • Looks at the whole dataset
  • Tries to detect patterns

Applications

Clustering

Clustering example

Species cluster

Color cluster

Origin cluster

Clustering models

• K Means:
  • Specify the number of clusters
• DBSCAN (density-based spatial clustering of applications with noise):
  • Specify what constitutes a cluster

Iris table

K-Means with 4 clusters

K-Means with 3 clusters

Ground truth

Anomaly detection

Detecting outliers

• Anomaly detection = detecting outliers
• Outliers = observations that differ from the rest

Outliers

Removing outliers

Some anomaly detection use cases

• Discover devices that fail faster or last longer
• Discover fraudsters that manage trick the system
• Discover patients that resist a fatal disease
• ...

Association

Association

Let's practice!

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