Description: 

The study centered on predicting quality defects early in production while aiming to minimize operational and financial costs to batch process manufacturing plants with relatively mature standards and defined specification limits.  

 Objectives: 

1. Arrive at an approach that applies to more than one type of industry.
2. Assemble an approach that uses Machine Learning algorithms for predicting quality. 
3. Overcome the challenge of structuring data sets for Predictive Quality with simplicity and clarity.
4. Explain how to surmount the data quantity issue.
5. Provide reusable code snippets for data transformation.