How can I perform linear regression with multiple variables using NumPy?

How can I perform linear regression with multiple variables using NumPy?

You can use NumPy's lstsq function to perform linear regression with multiple variables. First, you'll need to prepare your data in a matrix format.

What does the matrix format look like for multiple variables?

The matrix format should have your independent variables as columns and your dependent variable as a separate array. For example, if you have two independent variables, your matrix X would look like this: [[x1_1, x2_1], [x1_2, x2_2], ...].

How do I interpret the coefficients after fitting the model?

The coefficients represent the change in the dependent variable for a one-unit change in the corresponding independent variable, holding all other variables constant.

Can you show me a simple code example?

Sure! Here's a basic example:

import numpy as np

# Sample data
X = np.array([[1, 2], [2, 3], [3, 4]])  # Independent variables
Y = np.array([1, 2, 3])  # Dependent variable

# Perform linear regression
coefficients = np.linalg.lstsq(X, Y, rcond=None)[0]
print(coefficients)