// we run marksDf on the pipeline, so split marksDf
val Array(pipelineTrainingData, pipelineTestingData) = creditDf.randomSplit(Array(0.7, 0.3), seed)

// VectorAssembler and StringIndexer are transformers
// LogisticRegression is the estimator
val stages = Array(assembler, indexer, randomForestClassifier)

// build pipeline
val pipeline = new Pipeline().setStages(stages)
val pipelineModel = pipeline.fit(pipelineTrainingData)

// test model with test data
val pipelinePredictionDf = pipelineModel.transform(pipelineTestingData)
pipelinePredictionDf.show(10)
/*
 * output
+-------------+-------+--------+-------+-------+------+-------+----------+-----------+----------+----------+-----------------+------+---+----------+---------+-------+----------+----------+--------+-------+--------------------+-----+--------------------+--------------------+----------+
|creditability|balance|duration|history|purpose|amount|savings|employment|instPercent|sexMarried|guarantors|residenceDuration|assets|age|concCredit|apartment|credits|occupation|dependents|hasPhone|foreign|            features|label|       rawPrediction|         probability|prediction|
+-------------+-------+--------+-------+-------+------+-------+----------+-----------+----------+----------+-----------------+------+---+----------+---------+-------+----------+----------+--------+-------+--------------------+-----+--------------------+--------------------+----------+
|          0.0|    1.0|       6|      1|      6|  1198|      1|         5|          4|         2|         1|                4|     4| 35|         3|        3|      1|         3|         1|       1|      1|[1.0,6.0,1.0,6.0,...|  1.0|[10.7490734143103...|[0.53745367071551...|       0.0|
|          0.0|    1.0|       6|      4|      2|  3384|      1|         3|          1|         1|         1|                4|     1| 44|         3|        1|      1|         4|         1|       2|      1|[1.0,6.0,4.0,2.0,...|  1.0|[15.7968574432531...|[0.78984287216265...|       0.0|
|          0.0|    1.0|       9|      2|      3|  1366|      1|         2|          3|         2|         1|                4|     2| 22|         3|        1|      1|         3|         1|       1|      1|[1.0,9.0,2.0,3.0,...|  1.0|[12.1422936569444...|[0.60711468284722...|       0.0|
|          0.0|    1.0|      12|      1|      0|   697|      1|         2|          4|         3|         1|                2|     3| 46|         1|        2|      2|         3|         1|       2|      1|[1.0,12.0,1.0,0.0...|  1.0|[9.73041760825665...|[0.48652088041283...|       1.0|
|          0.0|    1.0|      12|      2|      4|   741|      2|         1|          4|         2|         1|                3|     2| 22|         3|        2|      1|         3|         1|       1|      1|[1.0,12.0,2.0,4.0...|  1.0|[10.6767838008722...|[0.53383919004361...|       0.0|
|          0.0|    1.0|      18|      1|      0|  1442|      1|         4|          4|         3|         1|                4|     4| 32|         1|        3|      2|         2|         2|       1|      1|[1.0,18.0,1.0,0.0...|  1.0|[10.0807119629414...|[0.50403559814707...|       0.0|
|          0.0|    1.0|      18|      2|      3|  3190|      1|         3|          2|         2|         1|                2|     1| 24|         3|        2|      1|         3|         1|       1|      1|[1.0,18.0,2.0,3.0...|  1.0|[11.9605710249254...|[0.59802855124627...|       0.0|
|          0.0|    1.0|      18|      4|      2|  2124|      1|         3|          4|         2|         1|                4|     1| 24|         3|        1|      2|         3|         1|       1|      1|[1.0,18.0,4.0,2.0...|  1.0|[14.1539496952876...|[0.70769748476438...|       0.0|
|          0.0|    1.0|      18|      4|      5|  1190|      1|         1|          2|         2|         1|                4|     4| 55|         3|        3|      3|         1|         2|       1|      1|[1.0,18.0,4.0,5.0...|  1.0|[14.1925605023117...|[0.70962802511558...|       0.0|
|          0.0|    1.0|      20|      4|      0|  2235|      1|         3|          4|         4|         3|                2|     2| 33|         1|        1|      2|         3|         1|       1|      2|[1.0,20.0,4.0,0.0...|  1.0|[13.7055893085556...|[0.68527946542778...|       0.0|
+-------------+-------+--------+-------+-------+------+-------+----------+-----------+----------+----------+-----------------+------+---+----------+---------+-------+----------+----------+--------+-------+--------------------+-----+--------------------+--------------------+----------+
*/