How to evaluate Data Science models ?

In today's Digital age, insights received from data science are extremely important to deliver the best customer experience. Data Scientists use various techniques such as Regression, SVM, Neural network, Nearest neighbor, Naive Bayes, Decision Tree and Ensemble models. These algorithms help to identify previously unrecognized patterns and trends hidden within vast amounts of structured and unstructured information. These patterns are used to create predictive models that try to forecast future behavior. These models have many practical business applications: predicting patients at risk, they help banks decide which customers to approve for loans, and marketers use them to determine which leads to target with campaigns. But how to determine if the predictive models you create are accurate, meaningful representations that will prove valuable to your organization?