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The primary aim of this research was to find a model that best predicts whichfallen angel bonds would either potentially rise up back to investment gradebonds and which ones would fall into bankruptcy. To implement the solution, wethought that the ideal method would be to create an optimal machine learningmodel that could predict bankruptcies. Among the many machine learning modelsout there we decided to pick four classification methods: logistic regression,KNN, SVM, and NN. We also utilized an automated methods of Google Cloud'smachine learning.  The results of our model comparisons showed that the models did not predictbankruptcies very well on the original data set with the exception of GoogleCloud's machine learning having a high precision score. However, ourover-sampled and feature selection data set did perform very well. This couldlikely be due to the model being over-fitted to match the narrative of theover-sampled data (as in, it does not accurately predict data outside of thisdata set quite well). Therefore, we were not able to create a model that we areconfident that would predict bankruptcies.  However, we were able to find value out of this project in two key ways. Thefirst is that Google Cloud's machine learning model in every metric and inevery data set either outperformed or performed on par with the other models.The second is that we found that utilizing feature selection did not reducepredictive power that much. This means that we can reduce the amount of data tocollect for future experimentation regarding predicting bankruptcies.

Fallen Angel Bonds Investment and Bankruptcy Predictions Using Manual Models and Automated Machine Learning