This is a credit risk analysis using two different machine learning models: Boosted Decision Tree and Boosted Logistic Regression. The main goal is to identify the possibility of a loss resulting from a borrower’s failure to repay a loan or meet contractual obligations.
Contact: solares.fs@gmail.com
For this project, we’re going to use the famous German Credit Data Set, already cleaned and organized (new column names and target variable in the first position). You can find the original data set at: https://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data) and the cleaned one in this repository.
DSA - Data Science Academy - Big Data Analytics with R and Microsoft Azure class notes. Retrieved from: https://www.datascienceacademy.com.br/course?courseid=analise-de-dados-com-r
Handling imbalanced datasets in machine learning. Retrieved from: https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28
The caret Package by Max Kuhn 2019-03-27. Retrieved from: http://topepo.github.io/caret/index.html
Practical Guide to deal with Imbalanced Classification Problems in R. Retrieved from: https://www.analyticsvidhya.com/blog/2016/03/practical-guide-deal-imbalanced-classification-problems/