A snippet on how to calculate mean squared error (MSE) from scratch.

calculate-mse.py

import numpy as np

actual = np.array([1, 2, 3, 4, 5])
predicted = np.array([1.1, 1.9, 2.7, 4.5, 6])

def mse(actual: np.ndarray, predicted: np.ndarray) -> float:
    differences = np.subtract(actual, predicted)
    squared_differences = np.square(differences)
    return np.mean(squared_differences)

mse(actual, predicted)
# 0.27199999999999996