Evaluate Bank Credit Risk Prediction Accuracy based on SVM Model in Azure Machine Learning
Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan and helps lower the risk of default on debt. To understand the risk involved, credit providers normally collect vast amounts of information on borrowers and apply various predictive and analytic techniques to analyze or to determine risk levels. In this project, we have used support vector machine (SVM) model which is a supervised learning technique. Here I am using 21 columns for prediction, some of them are - Age (numeric) Sex (text: male, female) Job (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled) Housing (text: own, rent, or free) Saving accounts (text - little, moderate, quite rich, rich) Checking account (numeric, in DM - Deutsch Mark) Credit amount (numeric, in DM) Duration (numeric, in month) Purpose(text: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others Credit Risk (Value target - Good or Bad Risk, 0 or 1) The model is created using the Two-Class Support Vector Machine and trained on the replicated (cost-sensitive) training data set which is created using R Script and then evaluate model. Results: The first column lists the machine learning algorithm used to generate a model. The second column indicates the type of training set. The third column contains the cost-sensitive accuracy value. Accuracy is 70% for SVM model.