Continuing from where we left, we delve deeper into how to develop machine learning (ML) algorithms using TensorFlow from a scikit-learn developer's perspective. If you'd like to know the reasons to move to TensorFlow, motivations, do read my earlier post for Reasons to move to TensorFlow and a simple classification program that highlights similarities of developing for scikit-learn and TensorFlow. In the earlier post, we compared the fit and predict paradigm similarities in scikit-learn and TensorFlow. In this post, I want to show we can develop a TensorFlow classification framework with Scikit-learn's data processing and reporting tools. This will give a good method to interweave both the frameworks to come up with a neat and concise framework.