recomand prices

import numpy as np

def normEqn(X, y):
    theta = np.zeros((X.shape[1], 1))
    theta = np.linalg.inv(X.T.dot(X)).dot(X.T).dot(y)
    return theta

def getPred():
    arr = np.loadtxt("buildings.csv",
                     delimiter=",", dtype=int)
    buildingsParams = arr[:, [0, 1]]  # 1239,3,229900
    prices = arr[:, [2]]

    ones = np.ones((prices.size, 1), dtype=int, order='C')
    buildingsParams = np.hstack((ones, buildingsParams))
    theta = normEqn(buildingsParams, prices)
    return theta

def main():

    theta = getPred()
    #predict
    area = int(input("area: "))
    rooms = int(input("rooms: "))
    X=np.array([1, area, rooms])
    price = X.dot(theta)
    print(f"price: {int(price[0])}$")

if __name__ == '__main__':
    main()