About this ebook
In this book we aim at applying machine learning models to classify the borrowers with and without loan default from a group of predicting variables and evaluate their performance.
As a part of building a model to predict loan default, we have submitted in detail the introduction of the problem, exploratory data analysis (EDA), data cleaning and pre-processing, model building, interpretation, model tuning, model validation, and final interpretation & recommendations.
Under the current project of loan default forming part of predictive analytics of business analytics and intelligence, we have studied research-based review parameters in detail which have also been annexed for ready reference as Annexure I. Data dictionary has been annexed as Annexure-2. R. Code for the same is provided at the URL which can be downloaded from www.drvvlnsastry.com/businessanalytics/data
The study finds out that logistic regression is the best model to classify those applicants with loan default.
Discover more
Ratings and reviews
- Flag inappropriate
About the author
A Graduate in Technology, Dr.V.V.L.N. Sastry, holds M.B.A, LL.M, ACMA, M.Sc in Information Systems and Management from University of Roehampton, Ph.D in Banking, Ph.D in Computer Science, Ph. D in Financial Management, and Ph.D in Criminal Law and Public Policy from Walden University, U.S.A. Sastry, brings over 20 years of experience in the banking, investment banking, software industry and law. He is the author of more than 1000 published articles on varied subjects in the areas of IT, Banking, Finance, Economics and Law. He also has authored several books in the said fields. Rated among the top researchers in the world wide applied academics by www.academia.edu, his applied research contributions are well received across the world.
