Difference between Regression and Classification problem?

Regression vs. Classification in Machine Learning

Difference between Regression and Classification

Regression Algorithm Classification Algorithm
In Regression, the output variable must be of continuous nature or real value. In Classification, the output variable must be a discrete value.
The task of the regression algorithm is to map the input value (x) with the continuous output variable(y). The task of the classification algorithm is to map the input value(x) with the discrete output variable(y).
Regression Algorithms are used with continuous data. Classification Algorithms are used with discrete data.
In Regression, we try to find the best fit line, which can predict the output more accurately. In Classification, we try to find the decision boundary, which can divide the dataset into different classes.
Regression algorithms can be used to solve the regression problems such as Weather Prediction, House price prediction, etc. Classification Algorithms can be used to solve classification problems such as Identification of spam emails, Speech Recognition, Identification of cancer cells, etc.
The regression Algorithm can be further divided into Linear and Non-linear Regression. The Classification algorithms can be divided into Binary Classifier and Multi-class Classifier.