Bank Credit
How to predict customer commitment to the bank according to his status using their data of cooperation with the bank as features.
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
