Classifications in R: Response Modeling/Credit Scoring/Credit Rating using Machine Learning Techniques

This article was written by Ariful Mondal. Artful is a senior manager, data science and big data analytics consultant at Tata Consultancy Services. This is an attempt to showcase some worked out examples of Machine Learning (ML) use German Credit Data. Though we have selected credit scoring problem as a case study in this article, the same process will be applicable for wide range of classification or regression problems "Response modeling", "Risk Management", "Attrition/Churn management", "Cross-Sell/Up-Sell", "usage Patterns", "Net Present Value", "Life Time Value", "Predictive Maintenance and condition based monitoring", "Warranty", "Reliability", "Failure Prediction", "Image/Video Processing", "Crime", "Medical Experiments", "Hidden pattern recognition" . The basic difference of traditional modeling and machine learning is that "in traditional modeling we intend to set up a modeling framework and try to establish relationships while in machine learning we allow the model to learn from the data by understanding the hidden patterns".