Machine Learning, Deep Learning, Stochastic Gradient, Data Science, Editors Pick.
np.round(linear_regression_pipeline.predict(to_predict), 0)[0] print("Predicted cooking time: " + str(prediction) + " minutes") r_squared = np.round(linear_regression_pipeline.score(feature_array, target_array).reshape(-1, 1)[0][0], 2) print("R-squared: " + str(r_squared)) feature_array = [[500,