We make use of regression models when we have to predict a continuous value rather than a discrete one. For example, let's say that a dataset contains the number of years of experience of an employee and the employee's salary. Based upon these two values, this model is trained and expected to make a prediction on the employee's salary based on their years of experience. Since the salary is a continuous number, we can make use of regression-based machine learning models to solve this kind of problem.
The various regression models we will discuss are as follows:
- Simple linear regression
- Multiple linear regression
- Polynomial regression
- Support vector regression
- Decision tree regression
- Random forest regression
