# Comparing Data and Algorithmic Modeling Approaches for Count Data

Senior

Mathematics

Biology

## List all Project Mentors & Advisor(s)

Dr. Christopher Mecklin; Dr. Gopal Nath

## Presentation Format

Oral Presentation

## Abstract/Description

Throughout the world there is numerous reasons a person may want to predict an outcome. A type of data that can be predicted is count data. As the name suggests count data involves the number of an occurrence or object that is in a countable form. An example of count data is the number of expected insurance claims to be received during a certain period of time. Another example is the anticipated number or articles a professor might publish. There are several approaches that can be taken in order to predict count data. There is the traditional statistical model's approach. These models are the Poisson distribution and the negative binomial distribution. A different procedure is to use computer algorithms also known as machine learning for predictions. Two specific algorithms are artificial neural networks (ANN) and k-nearest neighbors (KNN). To choose which method to use, the accuracy must be accessed for each approach.

## Spring Scholars Week 2022 Event

Honors College Senior Thesis Presentations

## Share

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Comparing Data and Algorithmic Modeling Approaches for Count Data

Throughout the world there is numerous reasons a person may want to predict an outcome. A type of data that can be predicted is count data. As the name suggests count data involves the number of an occurrence or object that is in a countable form. An example of count data is the number of expected insurance claims to be received during a certain period of time. Another example is the anticipated number or articles a professor might publish. There are several approaches that can be taken in order to predict count data. There is the traditional statistical model's approach. These models are the Poisson distribution and the negative binomial distribution. A different procedure is to use computer algorithms also known as machine learning for predictions. Two specific algorithms are artificial neural networks (ANN) and k-nearest neighbors (KNN). To choose which method to use, the accuracy must be accessed for each approach.