Classification and Regression both are the supervised learning algorithms in machine learning, and uses the same concept of training datasets for making predictions. The main difference between both the algorithms is that the output variable in regression algorithms is **Numerical** or **continuous**, whereas in Classification algorithm output variables are **Categorical** or **discrete**.

**Regression Algorithm:** A regression algorithm is about mapping the input variable x to some real numbers such as percentage, age, etc. Or we can say regression algorithms are used if the required output is continuous. **Linear regression is a famous example of the regression algorithm**.

Regression Algorithms are used in **weather forecasting, population growth prediction, market forecasting, etc**.

**Classification Algorithm:** A classification algorithm is about mapping the input variable x with a discrete number of labels such as true or false, yes or no, male-female, etc. Or we can say Classification algorithm is used if the required output is a discrete label. **Logistic regression** and **decision trees** are popular examples of a classification algorithm. The classification algorithm is used for **image classification, spam detection, identity fraud detection, etc**.