This model predicts which customers are likely to default in their loan payment in the future. Conducted descriptive analysis of dataset, handling missing data, balancing the dataset, performing feature selection, implementing various supervised machine learning algorithms such as random forests, SVM, decision trees, logistic regression and GBM. Evaluated model performance for all models using evaluation techniques such as confusion matrix, gain and ROC charts.
This analysis was conducting using R in Rstudio and the results were visualized by converting Rmakrdown files to html files.