Feature preparation

In section, Feature extraction of Chapter 2, Data Preparation for Spark ML, we have reviewed a few methods for feature extraction and discussed their implementation in Apache Spark. All the techniques discussed there can be applied to our data here, especially the ones for utilizing time series and feature comparison to create new features. For example, the customer satisfaction response change over time is considered as possibly an excellent predictor.

For this project, we will need to conduct both feature extraction and feature selection, which will allow us to utilize all the techniques discussed in Chapter 2, Data Preparation for Spark ML and also Chapter 3, A Holistic View on Spark.

The data merging part is also necessary, but its implementation is similar to what was described in the previous chapters, to be completed at ease.

Feature extraction

In the previous chapters, we used Spark SQL and R for feature extraction and, for this real-life project, we will try to use...