<jupyter_start><jupyter_text>Dataset<jupyter_code>import numpy as np
np.random.seed(42)
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
boston = datasets.load_boston()
x, y = boston.data[:, 1:3], boston.target

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
plt.scatter(x_test[:, 0], x_test[:, 1])
plt.show()

print(f"Min: {np.min(x_test, axis=0)}")
print(f"Max: {np.max(x_test, axis=0)}")
print(f"Mean: {np.mean(x_test, axis=0)}")
print(f"Std: {np.std(x_test, axis=0)}")<jupyter_output><empty_output>
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{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '567601', 'license': 'MIT', 'n_tokens_mistral': 260, 'n_tokens_neox': 257, 'n_words': 48, 'path': 'Chapter8_MetricsAndEvaluation/DataNormalization/Preparation.ipynb', 'repo_name': 'tomex74/UdemyML', 'size': '593'}