Created
October 18, 2018 19:18
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FlintCase6
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| from pyspark.ml.regression import LinearRegression | |
| from pyspark.ml.feature import VectorAssembler | |
| assembler = VectorAssembler( | |
| inputCols=["previous_day_return", "previous_day_decayed_return"], | |
| outputCol="features") | |
| output = assembler.transform(sp500_decayed_return).select('return', 'features').toDF('label', 'features') | |
| lr = LinearRegression(maxIter=10, regParam=0.3, elasticNetParam=0.8) | |
| model = lr.fit(output) |
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