In this tutorial, we set out to analyze the Amazon product dataset using SparkMLlib. The training data set includes ASIN, Brand Name, Category Name, Product Title, Image URL. For a detailed description of the Amazon product data, the reader can refer to Julian McAuley webpage.

In the previous lesson, we performed the basic steps of exploratory data analysis. This and the current lesson constitute important preprocessing steps before a model can be trained.

In the previous lesson, in our Transformation TODO list we implemented the StringIndexer() and SQLTransformer() transformers. In this lesson, we would implement the VectorAssembler() and Normalizer() and proceed to build our pipeline. Basically, a machine…

In the previous lesson, we created the pipeline. It is noteworthy that our ML pipeline contains vital workflow components comprising of the transformers and logistic regression estimator. We would need to fit our processed training dataset unto this pipeline…

In the previous lesson, we presented the training and testing of our model. This was on the basis of creating our ML pipeline. The following figure illustrates the workflow.