Xg3-3-1-1

from sklearn.datasets import load_boston

ds_boston = load_boston()

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Xg3-3-1-2

ds_boston.data

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ds_boston.data.shape

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ds_boston.feature_names

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ds_boston.target

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from sklearn.model_selection import train_test_split

(x_train, x_test, y_train, y_test) = train_test_split(
    ds_boston.data, ds_boston.target, test_size=0.3)

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print(x_train.shape)
print(y_train.shape)
print(x_test.shape)
print(y_test.shape)

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x_train

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from sklearn.linear_model import LinearRegression

lr = LinearRegression()

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lr.fit(x_train, y_train)

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lr.score(x_test, y_test)

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lr.predict(x_test[:1])

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y_test[:1]

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from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
scaler.fit(x_train)
x_train_scaled = scaler.transform(x_train)

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x_train_scaled

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