Credit Risk Prediction - Two Class Decision Forest
In this experiment two classification algorithm is used to make predict credit risk.
Used built-in Azure ML functionality, Python, R and SQL to select the features used for training a machine learning model. Then created, trained, and evaluated a first machine learning model to classify bank customers as good or bad credit risks. This is done by splitting the data to 70% train model and 30% test model, then predict the scores by 2 class decision tree and finally evaluate the result which held accuracy of 77.5% and F1 Score 0.799
