Response Modeling using Machine Learning Techniques in R

I have tried to exhibit credit scoring case studies with German Credit Data. This article includes detail programming of predictive modeling 1. Univariate And Bi-Variate Analysis 2. Information Value and Weight Evidence to access prediction power of variables 3. Multivariate Analysis and Dimension Reduction using Variable Clustering 4. Different Machine Learning Techniques and their performance evaluation using ROC, AUC and KS The basic difference of traditional modeling and machine learning is that "in traditional modeling we intend to setup a modelimg framework and try to establish relationships while in machine learning we allow the model to learn from the data by understanding the hidden patterns". Hence the first one requires analyst to have solid understanding of statistical techniques and business knowledge while the later one is more complex in nature and computational intensive, hence requires higher computation power of the systems and analyst needs to be tech savvy. Kindly note that while traditional techniques perform well on small to large amount of data, machine learning will certainly learn better on high-dimensional and complex data such as BigData setup. If you want to do more experiments and not sure where to get a problem definition or data to machine learning, you may explore the online machine learning repository here http://archive.ics.uci.edu/ml/.