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# create a sample data without the labels
sample_data_test = spark.createDataFrame([
(3.0, 'Z', 'S10', 40),
(1.0, 'X', 'E10', 20),
(4.0, 'A', 'S20', 10),
(3.0, 'A', 'S10', 20),
(4.0, 'X', 'D10', 30),
(1.0, 'Z', 'E10', 20),
(4.0, 'A', 'S10', 30),
], ['feature_1', 'feature_2', 'feature_3', 'feature_4'])
# transform the data using the pipeline
sample_data_test = model.transform(sample_data_test)
# see the prediction on the test data
sample_data_test.select('features', 'rawPrediction', 'probability', 'prediction').show()
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