Multiple and Polynomial Regression with Python

Multiple regression is a statistical technique that uses several independent variables to predict a single dependent variable. It's used to understand the relationship between multiple variables and how they impact the outcome. In Python, the statsmodels library provides a function called OLS (Ordinary Least Squares) which can be used to perform multiple regression.

Polynomial regression is a type of multiple regression where the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial. In Python, the scikit-learn library provides a class called PolynomialFeatures which can be used to transform an array of features into a polynomial feature space. This transformed data can then be used to fit a linear regression model.

Example:

Note that the above example is just for demonstration, and one would need to adjust the degree of the polynomial and other parameters as per the data.