import numpy as np

def gradient_descent(X, y, w, b, learning_rate, iterations):
    m = len(y)
    for _ in range(iterations):
        predictions = X.dot(w) + b
        errors = predictions - y
        gradient_w = X.T.dot(errors) / m
        gradient_b = np.sum(errors) / m
        w = w - learning_rate * gradient_w
        b = b - learning_rate * gradient_b
    return w, b

X = np.array([
    [1000, 2, 0],
    [2000, 1, 1],
    [3000, 3, 0],
    [2000, 4, 1],
    [4000, 2, 0],
])
actual_times = np.array([16, 29, 28, 35, 31])

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