The purpose of the project was to develop the model to predict the loan default probability issued by the bank to its borrowers.
The dataset on credit defaults was taken from the book: Machine Learning with R: Expert techniques for predictive modeling, 3rd Edition, The correlatio analysis and Modelling Analysis is based on the coding practices aquired from Business Science University R-Track.
In the first part of the analysis the data discovery as a well as the correlation between predictors and the target was performed to determine whether the given features could be usefull in predicting the probability of the default of the bank customers. As a result the features that have both considerable positive and negative correlation with defautlt metric were detected:
In the second part of the analysis the best models were selected to that has the best predicting power to detect the potenially defaulting loans. The h2o Automatic Machine learning was applied to pick the winning model. The leaderdashboard is presented below to display the perfomance metrcis of of the models used:
