Students who major or minor in Business Analytics are required to partake in a capstone project at the conclusion of their course work. The students will need to define their own objectives, find patterns in the data through descriptive analytics techniques and create predictive models using machine learning approaches. Teams compete to win cash prizes and certificates in the Business Analytics Poster Competition hosted by the Department of Business Analytics and Information Systems at Harbert College of Business each Spring and Fall Semester.
Presentations are judged on presentation skills, poster display, relevance of problem, clarity of results and implications of findings.
First Place: $1,500 and certificate of participation
Second Place: $1,000 and certificate of participation
Third Place: $700 and certificate of participation
People's Choice Award: $500 and certificate of participation
December 5, 2024 |
11:00 AM - 11:45 AM: |
11:50 AM - 1:30 PM: Location: Area surrounding Broadway Entertainment Space and Theatre - 1st floor - Horton-Hardgrave Hall Poster Judging and Display (Open to the University and Public) |
1:45 PM - 2:00 PM: |
Our corporate partners and data sponsors help make this event a real world exercise in business analytics. We would like to thank the following companies for participating:
The class will be working on different data-driven projects. The students will need to define their own objectives, find patterns in the data through descriptive analytics techniques and create predictive models using machine learning approaches.
1st Place
The Agile Analysts
Kyle Davis, Morgan Witcher, Katie Bohm, Evan Phillipson, and Sarah Cahill
2nd Place Tie
Team Rapid Recruits
Kensie Cimo, Jenna Roberts, Haylee Armstrong, Insos Reza Febri Mayor, and Sam Straub
2nd Place Tie
The Matchmakers
Natahn Hughes, Ally Galvan, and Drake Lowe
People's Choice Award
The Auburn Analysts
Avery Foto, Austin Blackwell, Lauren Crimin, and Kendall Brewer
TEAM - Dream Team "Bussin’ it Down: Tiger Transit Analytics "
First Place
Team Members: Houston Prewett, Shaun Gutmann, Jiayi Wang, Abby Johnson, Alex Romer
TEAM - Aerodynamic Analysts "Understanding safety and performance in f1 racing"
Second Place
Team Members:Hunter Lewis, Kelso Jacobson, Parker Elliot, Nelson Earley
TEAM - Skyline Chili Connoisseurs "Bengals Defensive Analysis"
Third Place
Team Members: Cameron Binney, Tyler Morris, Hudson Van Allen
TEAM - Bonnie Plants Analytics "Using Analytics to Improve Sell-Through"
People's Choice Award
Team Members:Dylan Fancher, Jake Richardson, Spencer Dunn, Matt Wilson, Andrew Jones
The students had to define their own objectives, obtain or collect the data, find patterns in the data through descriptive analytics techniques and create predictive models using machine learning approaches.
1. Understanding safety and performance in f1 racing - Formula One racing is a high-octane, high-intensity race tour that pits racers against each other with the most advanced and world’s highest-performing cars on some of the most complex tracks. In Formula One, one mistake could prove costly for a place on the podium and the driver's safety. With a select group of drivers and constructors dominating the sport and the associated risks of high-speed accidents, this project seeks to understand why accidents occur to help keep racers safe while balancing in-race strategies to help drivers reach the podium.
2. Hanes Size Mix Optimization - Our project aims to identify the most prominent characteristics of the data involved in determining size mix. Our project also helps determine the best level to understand optimal size mixes for Hanes Brand products. We applied clustering techniques to gain insights into the profile of the optimal product mix.
3. Bonnie Plants Analytics - Our team elected to work with Bonnie Plants to improve the Sell-Through. Bonnie Plants is one of the country's largest distributors of grown plants. Our data is based on 6 stations that service 6 different regions across the country. Stations house massive greenhouse gas sites, and units are shipped from station to stores to be sold. Common customers are outdoor retail companies, like Home Depot, Lowes, and Tractor Supply. Our project analyzes the number of plants sold relative to delivered, based on location, item category, and weather. We are looking at weather patterns for fiscal months to observe how various factors affect the seasonality of sell-through.
4. Smart Grid Energy Savers - Our project centers around using smart grid data to develop a more intelligent and efficient use of our daily resources. We are taking incredibly thorough and comprehensive data that outlines all the different energy uses throughout the homes to highlight areas of inefficiency to address. Many factors can play into this, and we are working to address all environmental factors that play a significant role. Moving forward, we hope that our work throughout this project can improve the use of smart grids as they grow to other cities across the nation and the globe.
5. Bengals Defensive Analysis - The Cincinnati Bengals’ defense had not been able to obtain consistent success within their division and in postseason games. This, coupled with the rapid development of high-powered offenses, requires a tremendous effort to achieve the goals of the Bengals. Our group aimed to provide the Bengals’ defensive coordinator with trends and visualizations to improve the defense's performance. We intended to show the coach actionable insights within the data that wouldn’t otherwise be seen without in-depth analysis. Some of our ideas included modeling the Bengals’ win-down percentage, identifying where explosive plays occur, creating heatmaps to highlight defensive weaknesses, and evaluating how the defense fares against top QBs and top WRs in the league. We hope that uncovering certain tendencies about their previous games can improve play calling, defensive reads, and the defense's overall performance.
6. Bussin’ it Down: Tiger Transit Analytics - Our project is focused on descriptive analysis and anomaly detection within Auburn’s Tiger Transit system. Our analysis finds patterns, trends, and anomalies within Tiger Transit’s operations using engine time, speed, boardings, and more variables. Using this data, we pinpoint shortcomings in Tiger Transit that could be why patrons are not satisfied. Additionally, by locating anomalies within the data, the system will be better aware of these anomalies and better equipped against further issues.
TEAM - Hunting Houses "Will the Bubble pop?"
First Place
Team Members: Kayla Gallman, Rebekah Rath, Sage Ellis, Hunter Lange, and Evan Atkinson.
TEAM - The Beatboxers "Predicting COVID-19 Vaccine Breakthrough"
Second Place
Team Members: Katie Craze, Megan Gibson, Blake Spradlin, Adam Camlic, Samuel Burnette)
TEAM - The Roots "Get to The Green: Analyzing Bonnie Plants Store Success"
Third Place
Team Members: Heath Varmette, Dax Wilson, Davis Hawk, Landry Stephens, Rob Griffin, Tucker Brant
Pictured with data sponsor representatives from Bonnie Plants, Alayna Priebe and Elizabeth Henslee
TEAM - Four Seam "Cold Pitches: The Effect of Weather on Baseball Metrics and Pitch Success"
People's Choice Award
Team Members: Alexa Hann, Rapley Hills, Hannah Hoven, Josh Petramale, Boston Smith, Aidan Stoffle
Pictured with Faculty Dr. Pei Xu and Dr. Pankush Kalgotra
The students had to define their own objectives, obtain or collect the data, find patterns in the data through descriptive analytics techniques and create predictive models using machine learning approaches.
1. Bonnie Plants Sales Prediction – Bonnies Plants has shared their sales data from The Home Depot and Lowes. In addition, the total foot traffic and demographic information about the location where stores are located is available. The objective is to identify the factors affecting sales using descriptive and predictive analytics models.
2. Insomnia condition among college students – The objective is to understand the relationship between mental disorders and insomnia among college students. The students collected data from an online health forum.
3. Olympians performance prediction – The purpose is to understand the relationship between the performance of the players based on their country of origin.
4. The Effect of Weather on Baseball Metrics and Pitch Success – Supported by AU Baseball, the students aim to identify the important sports-related and external factors that can predict pitch success.
5. Evaluating the performance of EBSCO customers – Sponsored by EBSCO, students are analyzing customer transaction data to find the differences between low and high-performing customers.
6. Predicting COVID-19 breakthrough infections in Alabama – Sponsored by the Alabama Department of Public Health, students are building models to compute the likelihood of a patient getting a breakthrough infection.
7. Will the bubble pop? - The housing market is in constant flux with houses being listed and sold every single day. This project examines the current housing market to determine if the conditions are similar with the housing market before the 2008 crash. Comparing these data points will allow us to forecast the potential of another crash in the current housing market.
TEAM - Model Behavior
First Place
Team Members: Nicholas Knautz, Chris Cox, Matthew Mayers, Grace Turney, and Alex Hamm.
Team members are pictured with course instructor Pankush Kalgotra (left) and department chair Uzma Raja (right).
TEAM - The Dream Team
Second Place
Team Members: Liqi Lu, Will Bowman, Sam Richardson, Zhongxing Chen and Christian Wilson
Team members are pictured with course instructor Pankush Kalgotra (left) and department chair Uzma Raja (right).
TEAM- The One and Only
Third Place
Team Members: Sam Easterling, Joshua Picott, Christian Conboy, Emma Meeks, Savannah Street.
Team members are pictured with course instructor Pankush Kalgotra (left) and department chair Uzma Raja (right).
There are seven teams in this class working on different data-driven projects. The students had to define their own objectives, obtain or collect the data, find patterns in the data through descriptive analytics techniques and create predictive models using machine learning approaches. A brief description of each dataset is given below.
View Spring 2022 Presentations
TEAM 8 - Auburn Analysts
First Place
Team members: Chloe Mikus, Isabel De Armas, Pierce Dickson, Brady Watts, and Anne
Hays Wright
TEAM 10 -Dill or No Dill
Second Place
Team members: Jack Ray, Danny Trainer, Kayla Taylor, Anthony Bostany, and Noah Vaughn
TEAM 13 - The Miner League
Third Place
Team Members: Frank Hudgins, Riley Spengeman, Mary Koch, Kyle Travelstead, Grace Crosson
TEAM 7 - BUAL Ballers
Peoples' Choice Award
Team members: Jiawei Tong, Tiancheng Jiang, Tanner Rowburrey, Madison Morrow, W. Buddy
Haas, Annabel Antoniak