Creating applications with the ability to learn from available data and making decisions based on the prediction results obtained from the learned models has become a trend-setting aspect in the software industry. Therefore, almost all applications that do predictive modeling uses one or more of these already available machine learning engines. With the ability to directly interact with these engines with simple API calls, intricacy of integration has alleviated rapidly. One of the common practices is training models with machine learning engines and building predictive APIs on top of them to retrieve prediction results for a new event. This article discusses how to train a machine learning model with WSO2 Machine Learner and create a predictive API with WSO2 API Manager with the trained model.

Predicting the Presidential Election is practically a national sport. However, traditional predictors – especially the talkshow hosts on Fox News – have historically been terrible at calling the next set of numbers. It took Nate Silver's exceptional statistical skill to show us that with public data, you could accurately predict the election down to the last winning percentage – if the mind doing the calculations was good enough. Artificial Intelligence has evolved exponentially over the years. We've gone from Deep Blue beating Gary Kasparov to DeepMind mastering Go.