How can I implement linear regression using NumPy?

How can I implement linear regression using NumPy?

You can implement linear regression in NumPy by using the least squares method. Would you like a code example?

Yes, please provide a code example.

Here's a simple implementation:

import numpy as np

# Sample data
X = np.array([[1], [2], [3], [4], [5]])
y = np.array([1, 2, 3, 4, 5])

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

# Calculating theta using the normal equation
theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)

print(theta_best)

This will give you the coefficients for the linear regression model.

What do the coefficients represent in this context?

The coefficients represent the slope and intercept of the linear regression line. The first coefficient is the intercept, and the second is the slope.