Credit Risk Classification using Random Forest | Machine Learning | Python
In this video we explore the robust capabilities of Random Forest algorithms in assessing credit risk. We navigate through a dataset of 1,000 bank customers, analyzing loan repayment patterns to build a Machine Learning model to classify potential loan requests, distinguishing between good and bad customers with remarkable accuracy.
Sklearn documentation for Random Forest:
https://scikit-learn.org/stable/modul...
Dataset and Source code on GitHub:
https://github.com/NeuronalLab/Credit...
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