Predictive Analysis of Classification Algorithms on Banking Data

The entire human activity is generating about 2.5 quintillion bytes of data per day. It is growing exponentially. The tremendous volume of data is getting generated by different banking services, including real-estate loan, deposit accounts services, credit card issuing service, commercial loan lending and many more. The bank data is used for business analytic that keeps eyes on past growth also Predictive analysis opens doors for future growth. For this project, during the predictive analysis, we used some historical data generated by an organization during loan processing. We intended to predict whether a new applicant will have granted a loan or not. The analysis was done by building Machine Learning(ML) models using different classification algorithms like Random Forest(RF), Logistic Regression(LR), Support Vector Machine(SVM), Decision Tree(DT), Gradient Boosting, XG Boost. The classifier models were trained and tested on hyperparameters as the performance of each model was analyzed using Confusion Matrix. We compared efficiencies using Recall, Precision, Accuracy and F1 score in a tabular form among all models. LR yields an F1 score of 0.90 and 0.62 for class 1 (approved) and class 0 (not Approved), respectively. Also, XGBoost gave F1 score of 0.89 and 0.64 for the same class 1 and 0.