Date on Honors Thesis
Spring 5-2022
Major
Mathematics
Minor
Biology
Examining Committee Member
Dr. Christopher Mecklin, Advisor
Examining Committee Member
Dr. Gopal Nath, Committee Member
Examining Committee Member
Dr. Manoj Pathak, Committee Member
Abstract/Description
Various techniques are used to create predictions based on count data. This type of data takes the form of a non-negative integers such as the number of claims an insurance policy holder may make. These predictions can allow people to prepare for likely outcomes. Thus, it is important to know how accurate the predictions are. Traditional statistical approaches for predicting count data include Poisson regression as well as negative binomial regression. Both methods also have a zero-inflated version that can be used when the data has an overabundance of zeros. Another procedure is to use computer algorithms, also known as machine learning, for predictions. Two specific algorithms used here are artificial neural networks (ANN) and k-nearest neighbors (KNN). This project aims to consider both traditional statistical modeling and algorithmic modeling to find which technique is the most accurate and therefore most effective. This will be accessed by using two datasets to test the assorted models.
Recommended Citation
Hack, Andraya, "Data and Algorithmic Modeling Approaches to Count Data" (2022). Honors College Theses. 113.
https://digitalcommons.murraystate.edu/honorstheses/113
Included in
Analysis Commons, Artificial Intelligence and Robotics Commons, Control Theory Commons, Data Science Commons, Numerical Analysis and Computation Commons, Numerical Analysis and Scientific Computing Commons, Other Applied Mathematics Commons, Programming Languages and Compilers Commons, Theory and Algorithms Commons