How can I perform multiple regression using NumPy in Python?

How can I perform multiple regression using NumPy in Python?

You can use NumPy's linear algebra module to perform multiple regression. First, you'll need to prepare your data.

What format should my data be in?

Your data should be in a 2D array format, where each row represents a sample and each column represents a feature.

Can you show me a code example?

Sure! Here's a simple example:

import numpy as np

# Sample data
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])  # Features
Y = np.array([1, 2, 2, 3])  # Target variable

# Adding a bias term
X_b = np.c_[np.ones((X.shape[0], 1)), X]  # Add x0 = 1 to each instance

# Performing the regression
theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(Y)
print(theta_best)

What does the output represent?

The output represents the coefficients of the regression model, which you can use to make predictions.