import numpy as np

def least_square_estimator(X, Y):
    """
    Compute beta (minimum cost computation)
    --------------------------
    (((X^T)X)^-1)((X^T)Y)

    Order
    --------------------------

    First = (((X^T)X)^-1)
    Second = ((X^T)Y)
    Final = (((X^T)X)^-1)((X^T)Y)

    Type
    --------------------------
    X : ndarray, shape(m_number, n_features)

    Y : ndarray, shape(m_number, )
    """
    return np.dot(np.linalg.inv(np.dot(X.T, X)), np.dot(X.T, Y))