Polynomial Regression Failure

I am trying to implement my own Polynomial Regression model from scratch, here's what I've written so far:

import numpy as np



class Polynomial:

    def __init__(self, eta=0.2, degree=1, epochs=100):
        self.eta = eta
        self.degree = degree
        self.coef = np.ones(shape = (self.degree,))
        self.epochs = epochs

    def fit_coef(self, X_train, y_train):
        temp_coef = np.ones(shape=(self.degree, ))
        size = len(X_train)
        for _ in range(3):
            for x, y in zip(X_train, y_train):
                arr = np.array([x**i for i in range(self.degree)])
                for i in range(self.degree):
                    err = np.sum(np.transpose(arr)*self.coef)*2/size
                    err -= y
                    err *= x**i
                    err *= self.eta
                    temp_coef[i] -= err
                    print(temp_coef[i])
                self.coef = temp_coef

After trying to fit my model to any examplatory dataset, I'm encountering a problem, coefficients of my model become NaN values, I've noticed that they growth very fast to big values. I can't explain this, so fix it. Majority of online tutorials, articles about Polynomial Regression focus on using sklearn or other packages so I haven't found solution. Are you able to help me inspect this issue?