Linear Regression Gradient Descent Python

linear_regression.py

class LinearRegressionGD(object):
    
    def __init__(self, l_rate = 0.1, n_iter =10000):
        self.l_rate = l_rate
        self.n_iter = n_iter               
        
    def fit(self, X, y, theta):
        self.theta = theta
        X_value = X[:,1].reshape(-1, 1)
        const = self.l_rate*(1/X.shape[0])        
        for i in range(0, self.n_iter):
            h = X.dot(self.theta)            
            self.theta[0] = self.theta[0]-const*sum(h-y)
            self.theta[1] = self.theta[1]-const*sum((h-y).transpose().dot(X_value))            
        return self.theta
    
    def predict(self, X):
        X_test = X[:, 1]
        predict_value = X_test*self.theta[1]+self.theta[0]
        return predict_value