here we have our actual and predicted values side by sideand you can see that they're not too bad they tend to be more or less in the same ballpark there you have ita linear regression model in action using spark using the new dataframe-based api for mllib more and moreyou'll be using these apis going forward with mllib in sparkso make sure you opt for these when you can alrightthat' mllib in sparka way of actually distributing massive computing tasks across an entire cluster for doing machine learning on big datasets sogood skill to have let' move on summary in this we started with installing sparkthen moved to introducing spark in depth while understanding how spark works in combination with rdds we also walked through various ways of creating rdds while exploring different operations we then introduced mlliband stepped through some detailed examples of decision trees and -means clustering in spark we then pulled off our masterstroke of creating search engine in just few lines of code using tf-idf finallywe looked at the new features of spark in the next we'll take look at / testing and experimental design