Linear Regression from scratch

import numpy as np

class LinearRegression: #Data #Initialize data # compute gradient #update weights #predict

def fit(self, X, y, lr=0.001, iter=1000, thres=0.001):
    #initialize weight
    self.weights = np.zeros(X.shape[1] + 1)
    #X Add bias to X
    X = self.add_bias(X)
    
    while True:
        gradient =  np.dot((y - self.predict(X, False)), X) #compute the gradient and update weight
        update = lr*gradient
        self.weights = self.weights + update
        
        if np.max(np.abs(update))< thres:break
    
def predict(self,X, no_bias = True):
    if no_bias :
        X = self.add_bias(X)
    return np.dot(X, self.weights)

def add_bias(self, X):
    return np.insert(X, 0,np.ones(X.shape[0]), axis=1)