International Journal on Science and Technology

E-ISSN: 2229-7677   •   Impact Factor: 9.88

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 16 Issue 2 April-June 2025 Submit your research before last 3 days of June to publish your research paper in the issue of April-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 16 Issue 2
2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

A Distributed Machine Learning Framework for Large-Scale Credit Card Delinquency Prediction Using Apache Spark

Author(s) Anirudh Reddy Pathe
Country United States
Abstract With high accuracy and efficiency, in the rapidly evolving financial sector, the prediction of credit card delinquency is imperative to mitigate risk and maintain proper customer relationships. It aims to introduce a robust framework of distributed machine learning using Apache Spark, which can enhance large-scale predictive capabilities for the detection of credit card delinquency. Utilizing the scalable computing capabilities of Apache Spark, the package combines diverse advanced machine learning models with logistic regression, random forests, gradient boosting, and neural networks to address complex high-dimensional datasets often associated with financial transaction data. Such models have been deliberately chosen and optimized for their capabilities to be applied in the unified predictive model so that better accuracy and a reduction in prediction times may be achieved. This is the ensemble approach, which, due to Spark's efficient data handling and processing capabilities, allows for real-time analytics and scalable learning, which is important in handling voluminous and continuously growing financial datasets.
Keywords Distributed Machine Learning, Apache Spark, Credit Card Delinquency Prediction, Ensemble Methods, Neural Networks, Financial Risk Management, Big Data Analytics, Real-time Data Processing
Published In Volume 14, Issue 3, July-September 2023
Published On 2023-07-03
Cite This A Distributed Machine Learning Framework for Large-Scale Credit Card Delinquency Prediction Using Apache Spark - Anirudh Reddy Pathe - IJSAT Volume 14, Issue 3, July-September 2023. DOI 10.5281/zenodo.14613823
DOI https://doi.org/10.5281/zenodo.14613823
Short DOI https://doi.org/g8x2wk
View / Download PDF File

Share this



CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJSAT DOI prefix is
10.71097/IJSAT

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 15 Isu 4Cover Page Vol 15 Isu 3Cover Page Vol 15 Isu 2

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJSAT
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us Message on WhatsApp +91-9687-182-185 editor@ijsat.org