Poster Presentation - A Winning Capstone Experience

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.  

Judging Criteria

Presentations are judged on presentation skills, poster display, relevance of problem, clarity of results and implications of findings.

Winners

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

When, Where, and What to Expect

Spring 2025 Projects

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.

Past Presentations and Winners

• Spring 2024

  Spring 2024

  
  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

• Fall 2023

  Fall 2023

  

  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 

   

   

  Fall 2023 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.

  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.

   
• Spring 2023

  Spring 2023

   

  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

  Spring 2023 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.

  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.

• Fall 2022

  Fall 2022

  View Fall 2022 Presentations

  

  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).

  Fall 2022 Projects

  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.

  1. Bonnie Plants Sales Prediction – Bonnies Plants has shared their two years of sales data from Home Depot and Lowes with the BASY department. In addition, the total foot traffic and demographics information about the location where stores are located is available. The objective is to identify the factors affecting the sales using descriptive and predictive analytics models.
  2. Healthcare analytics – Focusing on the condition of epilepsy, students have obtained the medical conditions of epilepsy patients from an online health website. The objective is to find the health disparities among epilepsy patients by gender and age.
  3. Sleep Study - We got access to the sleep-related data. The dataset contains sequential information about sleep stages recorded overnight from more than 2,600 patients with heart related disorders. The objective is to create classification models to diagnose the heart diseases using the sleep patterns. (Acknowledgements - Dr. Rupesh Agrawal, Northern Kentucky University)
  4. Sports Analytics – We had access to NCAA basketball games data. In this unique dataset, the detail of each game is available including the different plays every second in the game. The objective is to understand the trends in the events over years across all teams. (Acknowledgements - Dr. David Paradice)
  5. Game days and Crime rate – The objective is to find a relationship between the crime rate and the game days. The students have collected data about the crimes and game schedules from multiple sources.
  6. American Hospital Association – The objective is to find the relationship between the use of technology by hospitals across all the regions in the US and the satisfaction of their employees. The BASY department has access to the unique AHA datasets for 13 years.
• Spring 2022

  Spring 2022

  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

   

  
  
  

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