Supervised Learning:

Supervised learning is used when we have a labeled dataset, meaning that each input has a corresponding output. The goal is to learn a mapping from inputs to outputs.

Common Algorithms:

1. Linear Regression: Used for predicting continuous values.
2. Logistic Regression: Used for binary classification problems.
3. Decision Trees: Simple models that split data into branches based on feature values.

Unsupervised Learning:

Unsupervised learning is used when we only have input data and no corresponding output labels. The goal is to find patterns or structure in the data.

Common Algorithms:

1. 1. k-means clustering – Divides data into K clusters based on feature similarity.
   2.  Hierarchical clustering: Builds a hierarchy of clusters by progressively merging or splitting them.
   3. principle component analysis: Reduces the dimensionality of data while preserving as much variability as possible.