Regression is predictive modeling. It is a statistical method, used in finance, investment, and other disciplines, that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables).
Regression helps investment and financial managers to value assets and understand the relationships between variables, such as commodity prices and the stocks of businesses dealing in those commodities.

Notes:

• The Reader (Dataset) should be connected to the algorithm.
• Missing values should not be present in any rows or columns of the reader. To check whether there are any missing values, use Descriptive Statistics algorithm. Refer to Descriptive Statistics.
• If missing values are present, impute them using Missing Value Imputation algorithm on the dataset. It is under Model Studio in Data Preparation. Refer to Missing Value Imputation.
• The dependent or output variable must be continuous data type.

List of Regression Algorithms

• Extreme Gradient Boost
• MLP Neural Network
• Poisson Regression
• Polynomial Regression
• k Nearest Neighbor
• Lasso Regression
• Linear Regression
• Ridge Regression
• Support Vector Machine