GitHub - AtomMe/PHBS_TQFML: This project is created for 2017-18 Module 3 (Spring), Topics in Quantitative Finance: Machine Learning for Finance

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This project is created for 2017-18 Module 3 (Spring), Topics in Quantitative Finance: Machine Learning for Finance

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Credit_VaR_Estimation.ipynb		Credit_VaR_Estimation.ipynb
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Bank Marketing Data Set: UCI, Download
Write a Jupyter notebook
- load data (bank.csv, smaller sample), normalize, and devide training/test sets
- randomly select 2 or 3 features
- apply the methods covered in Ch. 3 with SK-learn (logistic regress, SVM, decision tree, etc)
- check the accuracy and plot the outcome
- repeat above to find better feature