R-Analytics

Note. This article was left as reference, for an improved version, go to:  http://laranikalranalytics.blogspot.com/2021/03/updated-using-r-and-h2o-to-identify.html Introduction: We will identify anomalous products on the production line by using measurements from testing stations and deep learning models. Anomalous products are not failures, these anomalies are products close to the measurement limits, so we can display warnings before the process starts to make failed products and in this way the stations get maintenance.  Before starting we need the next software installed and working: - R language installed. - All the R packages mentioned in the R sources.( On my GitHub  ) - Testing stations data, I suggest to go station by station. - H2O open source framework. - Java 8 ( For H2O ). Open JDK:  https://github.com/ojdkbuild/contrib_jdk8u-ci/releases - R studio. Get your data. About the data: Since I cannot use my real data, for this article I am using  SECOM Data Set