Bank Credit

July 25, 2016

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Algorithms

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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