The banks are having a bit of trouble with debt at the moment. They have lent lots of money to people who promised to pay it back, and then didn’t. In the future, they would like to avoid lending to the kind of person who won’t pay back the loan, and that is where you come in. We have got some data from a bank describing 2000 of its loan customers. The data also tells us whether or not each customer repaid the loan. The question is simple – Can we predict who will repay the loans and who won’t?
Describe what you did with the data prior to the modelling process (data cleaning). Show histograms of the one example variable before and after any pre-processing that you carried out. If you corrected any mis-typed entries in the data, report what you changed. Carry out descriptive analysis and explain each what they represent, use as many graph as possible and provide the descriptions
Machine learning Modelling
Give a detailed technical description of the techniques and the way the models are represented, which machine learning you use and why. Include one diagram showing the structure of each type of model that you build. Describe what hyper parameters may be changed and what effect this has. If you varied the hyper parameters of a model, show how this impacted on the results. Describe how you split the data for training, validation and testing purposes. Be methodical and record each result. Explain the test and pay attention to overfitting and underlining. Show an ROC curve for the decision as to whether or not a loan will be repaid and describe what the curve shows.
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