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This article mainly introduces the example analysis of LinearRegression predicted by spark mllib, which is very detailed and has certain reference value. Friends who are interested must finish it!

The relationship between Commodity Price and Consumer input

Commodity demand (y, ton), price (x1, yuan), consumer income (x2, yuan)

Yx1x251181272113231834

Set up the demand function: y = ax1+bx2

Run the following package spark.regressionAnalysis/** * linear regression code to establish the relationship between commodity prices and consumer input. * Forecast prices * / import org.apache.log4j. {Level, Logger} import org.apache.spark.mllib.linalg.Vectorsimport org.apache.spark.mllib.regression. {LabeledPoint, LinearRegressionWithSGD} import org.apache.spark. {SparkConf SparkContext} object LinearRegression {val conf = new SparkConf () / / create environment variable .setMaster ("local") / / set localization handler .setAppName ("LinearRegression") / / set name val sc = new SparkContext (conf) / / create environment variable instance def main (args: Array [String]) {val data = sc.textFile (". / src/main/spark/regressionAnalysis/lr.txt") / / get the dataset path val parsedData = data.map {line = > / / start processing the dataset val parts = line.split ('|') / / partition LabeledPoint according to the comma (parts (0) .dataset Vectors.dense (parts (1). Split (','). Map (_ .toDouble))}. Cache () / / transform data format / / LabeledPoint, numIterations, stepSizeval model = LinearRegressionWithSGD.train (parsedData, 2,0.1) / / build the model val result = model.predict (Vectors.dense (1) 3)) / / through the model prediction model println (model.weights) println (model.weights.size) println (result) / / print the prediction results}}

Lr.txt

| 5 | 1Jing 18 | 1JI 27 | 2113 | 2318 | 3P4 results are shown in the figure.

The above is all the contents of the article "sample Analysis of LinearRegression for spark mllib Forecast". Thank you for reading! Hope to share the content to help you, more related knowledge, welcome to follow the industry information channel!

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