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@icexelloss
Created October 18, 2018 19:18
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FlintCase6
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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