def linear_regression_prediction(
    train_dt: list, train_usr: list, train_mtch: list, test_dt: list, test_mtch: list
) -> float:
    x = np.array([[1, item, train_mtch[i]] for i, item in enumerate(train_dt)])
    y = np.array(train_usr)
    beta = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose(), x)), x.transpose()), y)
    return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2])