PySpark – PySpark MLLib Table Of Contents: What is PySpark MLlib? Two APIs in MLlib Why Use MLlib? Key Features Example ML Pipeline (End-to-End) Commonly Used Classes When to Use PySpark MLlib? (1) What Is PySpark MLLib? (2) Two APIs in MLlib (3) Why Use MLlib? (4) Key Features (5) What is a PySpark Pipeline? model.transform(data) model = pipeline.fit(data) from pyspark.ml import Pipeline from pyspark.ml.feature import StringIndexer, VectorAssembler from pyspark.ml.classification import LogisticRegression # Step 1: Convert label to numeric indexer = StringIndexer(inputCol="purchased", outputCol="label") # Step 2: Assemble features assembler = VectorAssembler(inputCols=["age", "salary"], outputCol="features") # Step 3: Model lr = LogisticRegression(featuresCol="features",

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