Clearance Date Prediction Using Machine Learning Techniques

Author:

Rao Madhuri1ORCID,Senapati Ankit2,Kumar Kulamala Vinod2,Bokhare Anuja1

Affiliation:

1. Symbiosis Institute of Computer Studies and Research, Symbiosis International University (Deemed), India

2. Siksha ‘O' Anusandhan (Deemed), India

Abstract

Machine learning is the cutting-edge technology in today's corporate world, making it the first choice for prediction or calculated suggestions relying on heavy amount of data. As companies are evolving towards technological advancement, they are trying to gather as much statistical knowledge as possible regarding their customers and trying to analyze and use that knowledge towards the firm's growth. Machine learning being the top-most of its genre provides the pathway to all of those technological achievements like predictions, statistical analysis, success rate of each customer companies, etc. Machine learning techniques such as linear regression (LR), XGBoost, random forest, and decision tree can be useful for the prediction problems. Here in this work, the authors use data pre-processing and feature selection before applying these machine learning models for predicting the clearance due date.

Publisher

IGI Global

Reference19 articles.

1. TensorFlow: large-Scale Machine Learning on Heterogeneous Distributed Systems.;M.Abadi,2016

2. AlpaydinE. (2016). Machine learning: the new AI (The MIT press essential knowledge series). The MIT Press.

3. Prediction of credit card defaults through data analysis and machine learning techniques

4. Chen, T., He, T., Benesty, M., & Khotilvoich, V. (2019). xgboost: eXtreme Gradient Boosting. R package version 0.82.1

5. Very Fast C4.5 Decision Tree Algorithm

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3