Write a Python function that performs linear regression using the normal equation. The function should take a matrix X (features) and a vector y (target) as input, and return the coefficients of the linear regression model. Round your answer to four decimal places, -0.0 is a valid result for rounding a very small number.
Examples
Example 1:
Input:
X = [[1, 1], [1, 2], [1, 3]], y = [1, 2, 3]Output:
[0.0, 1.0]Explanation: The linear model is y = 0.0 + 1.0*x, perfectly fitting the input data.
Starter Code
import numpy as np
def linear_regression_normal_equation(X: list[list[float]], y: list[float]) -> list[float]:
# Your code here, make sure to round
return thetaPython3
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