import numpy as np from featureScaling import featureScale from regression import Regression from sklearn.datasets import load_boston boston = load_boston() X = boston['data'] y = boston['target'] feature_names = boston['feature_names'] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0) reg = Regression() reg.gradientDescent(X_train, y_train, None, 0.05) print('test data: ',y_test[10:15]) print('predicted data: ',reg.predict(X_test[10:15])) reg.normalEquation(X_train, y_train) print('test data: ',y_test[10:15]) print('predicted data: ',reg.predict(X_test[10:15]))