Running your framework code on Amazon SageMaker
We will start from a vanilla scikit-learn program that trains and saves a linear regression model on the Boston Housing dataset, which we used in Chapter 4, Training Machine Learning Models:
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score
import joblib
data = pd.read_csv('housing.csv')
labels = data[['medv']]
samples = data.drop(['medv'], axis=1)
X_train, X_test, y_train, y_test = train_test_split(
samples, labels, test_size=0.1, random_state=123)
regr = LinearRegression(normalize=True)
regr.fit(X_train, y_train)
y_pred = regr.predict(X_test)
print('Mean squared error: %.2f'
% mean_squared_error(y_test, y_pred))
print('Coefficient of determination: %.2f'
% r2_score(y_test, y_pred...
