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August 18, 2022 15:48
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linear regression scala
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// to start a spark session | |
import org.apache.spark.sql.SparkSession | |
// to use lineer regression model | |
import org.apache.spark.ml.regression.LinearRegression | |
//set logging to level of ERROR | |
import org.apache.log4j._ | |
Logger.getLogger("org").setLevel(Level.ERROR) | |
//start a spark Session | |
val spark = SparkSession.builder().getOrCreate() | |
//read data file | |
val data = spark.read.option("header","true").option("inferSchema","true").format("csv").load("Clean_Ecommerce.csv") | |
//check the schema | |
data.printSchema | |
//first row of the data | |
data.head(1) | |
//get column names | |
val columnNames = data.columns | |
//get first row of data | |
val firstRow = data.head(1)(0) | |
//loop through columns and print the data for each column on first row | |
for(i <- Range(1, columnNames.length)){ | |
println(s"Column: ${columnNames(i)} | Data: ${firstRow(i)}") | |
} | |
import org.apache.spark.ml.feature.VectorAssembler | |
import org.apache.spark.ml.linalg.Vectors | |
//("target label", "features") | |
val df = (data.select(data("Yearly Amount Spent").as("label"), | |
$"Email", $"Avatar", $"Avg Session Length", $"Time on App", | |
$"Time on Website", $"Length of Membership")) | |
//df.printSchema | |
//create an assembler | |
val assembler = (new VectorAssembler().setInputCols(Array( | |
"Avg Session Length", "Time on App", | |
"Time on Website", "Length of Membership")).setOutputCol("features")) | |
//get output | |
val output = assembler.transform(df).select($"label",$"features") | |
//lineer regression model | |
val lr = new LinearRegression().setMaxIter(100).setRegParam(0.3).setElasticNetParam(0.8) | |
//training | |
val model = lr.fit(output) | |
//coefficient and intercept of the line | |
println(s"Coefficients: ${model.coefficients} Intercept: ${model.intercept}") | |
//summary of the model | |
//predictions | |
model.summary.predictions.show() | |
//residuals | |
model.summary.residuals.show() | |
//Root mean square error | |
println(s"RMSE: ${model.summary.rootMeanSquaredError}") | |
//mean square error | |
println(s"MSE: ${model.summary.meanSquaredError}") | |
//r2 coefficient | |
println(s"R2: ${model.summary.r2}") |
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