Supervised Learning - Prediction Week 3 Challenge

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
df_train = pd.read_csv("data.csv")
# getting the values for X
X = df_train.iloc[:,0]
# getting the values for y
y = df_train.iloc[:,1]
print(X, y)
def gradient_descent(X, y, lr=0.001, epoch=1000):
    
    '''
    X is the features
    y is output
    lr is the learning rate
    epoch is the number of times our parameters get updated
    '''
    
    m, b = 0, 0 # parameters
    mse = [] # lists to store learning process
    N = len(X) # number of samples
    
    for _ in range(epoch):
                
        f = y - (m*X + b)
    
        # Updating m and b
        m -= lr * (-2 * X.dot(f).sum() / N)
        b -= lr * (-2 * f.sum() / N)
        
#        calculating and storing the value of mse
        mse.append(mean_squared_error(y, (m*X + b)))        
    
    return m, b, mse

m,b,mse = gradient_descent(X,y)
print(mse)