Data Preprocessing, Machine Learning
Accountability Accounting
2021
Project Description
Accountability Accounting, an investment bank, was interested in offering a new cryptocurrency investment portfolio for its customers. The company, however, is lost in the vast universe of cryptocurrencies. So, they asked us to create a report that includes what cryptocurrencies are on the trading market and how they could be grouped to create a classification system for this new investment.
The data was not ideal, so it needed to be processed to fit the machine learning models. Since there is no known output, we decided on unsupervised learning. To group the cryptocurrencies, we decided on a clustering algorithm.
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Crypto Analysis
Unsupervised Machine Learning: Clustering
Credit Risk Analysis
The Easy Ensemble AdaBoost Classifier was best at predicting high risk credit applications in comparison to the other models. With a 0.925 accuracy and a 0.91 recall score, both Easy Ensemble AdaBoost and Random Forest Classifiers outperformed the Resampling techniques in accurately predicting high-risk credit card applicants.
Neural Network and Deep Learning
Overall results of the deep learning classification problem was 73% accuracy.
