gradientdescent.py

from sklearn.datasets import make_regression
X, y = make_regression(n_samples=100, n_features=1, noise=0.1)

def LinearRegression(X, y, m_current=0, b_current=0, epochs=2000, learning_rate = 0.001):
    N = float(len(y))

    for i in range(epochs):
        y_current = m_current * X + b_current
        m_gradient = (-2/N) * sum(y-y_current) * X 
        b_gradient = (-2/N) * sum(y-y_current)
        m_current = m_current - (learning_rate * m_gradient)
        b_current = b_current - (learning_rate * b_gradient)  
    return m_current, b_current

m, b = LinearRegression(X, y) 
print(m , b)