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/modules/generated/sklearn.ensemble.RandomForestClassifier.html
Dataset and Source code on GitHub:
https://github.com/NeuronalLab/Credit-Risk-Assessment_Random-Forest_Python/tree/main
#machinelearning #datascience #python #randomforest #creditrisk #sklearn #machinelearningalgorithm #machinelearningwithpython #machinelearningproject #machinelearningtutorialforbeginners #machinelearningfullcourse #machinelearningtraining #machinelearningbasics
