Open AccessExploratory analysis on prediction of loan privilege for customers using random forest
Author(s) -
K. Ulaga Priya,
Smt Pushpa,
K. Kalaivani,
A Sartiha
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.21.12399
Subject(s) - random forest , loan , computer science , judgement , machine learning , default , set (abstract data type) , artificial intelligence , task (project management) , supervised learning , test set , data mining , finance , business , artificial neural network , engineering , systems engineering , political science , law , programming language
In Banking Industry loan Processing is a tedious task in identifying the default customers. Manual prediction of default customers might turn into a bad loan in future. Banks possess huge volume of behavioral data from which they are unable to make a judgement about prediction of loan defaulters. Modern techniques like Machine Learning will help to do analytical processing using Supervised Learning and Unsupervised Learning Technique. A data model for predicting default customers using Random forest Technique has been proposed. Data model Evaluation is done on training set and based on the performance parameters final prediction is done on the Test set. This is an evident that Random Forest technique will help the bank to predict the loan Defaulters with utmost accuracy.