Building a Process Output Optimization Solution using Multiple Models, Ensemble Learning and a Genetic Algorithm.

Machine Learning (ML), a branch of Computer Science that focuses on drawing insights and conclusions by examining data sets, is an increasingly popular discipline today in resolving enterprise business issues. However the field is vast and consists of numerous algorithms and approaches. Data sets are also often complex and require to be pre-processed before an ML algorithm can be'trained' to learn from such data. For a particular problem domain and data set, defining the pre-processing technique and selecting the ML algorithm (or set of algorithms) is still largely'an art rather than a science' depending on the knowledge and skills of the expert/data scientist in question. With time this will change and scientific guiding principles/best practices will emerge to pre-process data and to select appropriate algorithms for a particular problem domain - as the discipline matures.