January 2018
Intermediate to advanced
470 pages
11h 9m
English
Content preview from Scala Machine Learning Projects
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Step 1 - Import packages, load, parse, and explore the movie and rating dataset
We will load, parse, and do some exploratory analysis. However, before that, let's import the necessary packages and libraries:
package com.packt.ScalaML.MovieRecommendation import org.apache.spark.sql.SparkSession import org.apache.spark.mllib.recommendation.ALS import org.apache.spark.mllib.recommendation.MatrixFactorizationModel import org.apache.spark.mllib.recommendation.Rating import scala.Tuple2 import org.apache.spark.rdd.RDD
This code segment should return you the DataFrame of the ratings:
val ratigsFile = "data/ratings.csv"val df1 = spark.read.format("com.databricks.spark.csv").option("header", true).load(ratigsFile) val ratingsDF = df1.select(df1.col("userId"), ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.