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A Comparison of Machine Learning Techniques and Logistic Regression Method for the Prediction of Past-Due Amount
Grey Literature from PhD Candidates
  • Jie Hao, Kennesaw State University
  • Jennifer L. Priestley, Kennesaw State University
Department
Statistics and Analytical Sciences
Submission Date
1-1-2016
Abstract

The aim of this paper to predict a past-due amount using traditional and machine learning techniques: Logistic Analysis, k-Nearest Neighbor and Random Forest. The dataset to be analyzed is provided by Equifax, which contains 305 categories of financial information from more than 11,787,287 unique businesses from 2006 to 2014. The big challenge is how to handle with the big and noisy real world datasets. Among the three techniques, the results show that Logistic Regression Method is the best in terms of predictive accuracy and type I errors.

Citation Information
Jie Hao and Jennifer L. Priestley. "A Comparison of Machine Learning Techniques and Logistic Regression Method for the Prediction of Past-Due Amount" (2016)
Available at: http://works.bepress.com/jennifer_priestley/29/