veronicahurtado / fintech-module12-challenge

Supervised Machine Learning

Case Study

Please refer to the Report file and Jupyter notebook credit_risk_resampling.ipynb file.

Technical Environment

This tool utilises the following technologies:

• Pandas Documentation
• NumPy Documentation
• Scikit learn Documentation
• Imbalanced learn Documentation

Glossary

Accuracy is a measure of how often the model is correct. The ratio of correctly predicted observations to the total number of observations. It doesn't always communicate how precise the model is. Accuracy can be very susceptible to imbalanced classes.

(TP + TN) / (TP + TN + FP + FN)

Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. High precision relates to a low false positive rate.

TP / (TP + FP)

Recall is the ratio of correctly predicted positive observations to all predicted observations for that class.

TP / (TP + FN)

Classification Report is used to identify the Precision, Recall and Accuracy of a model for each given class.

Confusion Matrix is used to identify the Model's recall. If FNs are very undesirable then it is best to use a Model with high recall.

Source: Monash University, FinTech Boot Camp learning material.