def gradient_descent(x_list, y_list, w, b, learning_rate, iterations):
    m = len(x_list)

    for _ in range(iterations):
        slope_w = 0
        slope_b = 0

        for i in range(m):
            prediction = w * x_list[i] + b
            error = prediction - y_list[i]
            slope_w += error * x_list[i]
            slope_b += error

        slope_w = slope_w / m
        slope_b = slope_b / m

        w = w - learning_rate * slope_w
        b = b - learning_rate * slope_b

    return w, b


distances = [1, 2, 3, 4, 5]
actual_times = [12, 17, 22, 27, 32]

final_w, final_b = gradient_descent(distances, actual_times, w=0, b=0, learning_rate=0.01, iterations=10000)

print("Final w:", final_w)
print("Final b:", final_b)

# Final w: close to 5
# Final b: close to 7

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