What is the difference between supervised and unsupervised learning?

demouse

Supervised Learning:
In supervised learning, the model is trained on a labeled dataset, which means the input data already has the correct output.

Goal: Learn a mapping from inputs to outputs.

Examples: Classification (spam detection), Regression (house price prediction).

Algorithms: Linear Regression, Random Forest, Support Vector Machines, etc.

Unsupervised Learning:
In unsupervised learning, the model is trained on unlabeled data — it must find patterns or structures on its own.

Goal: Discover hidden patterns or groupings in data.

Examples: Clustering (customer segmentation), Dimensionality reduction (PCA).

Algorithms: K-Means, DBSCAN, PCA, Autoencoders.