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        Capstone Experience

        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

        December 5, 2024

        11:00 AM - 11:45 AM:
        Location: Broadway Entertainment Space and Theatre - 1st floor -
        Horton-Hardgrave Hall
        Networking lunch with students, faculty, and corporate guests.
        (Invitation only)

        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:
        Location: Broadway Entertainment Space and Theatre - 1st floor - Horton-Hardgrave Hall

        Awards Ceremony

        Corporate partners and data sponsors 

        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:

        agileonelogo

        Bonnie Plants

        regionslogo

        Fall 2024 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 winners, The Agile Analysts
          1st Place 

          The Agile Analysts
          Kyle Davis, Morgan Witcher, Katie Bohm, Evan Phillipson, and Sarah Cahill

          2nd Place Tie, The Rapid Recruits
          2nd Place Tie

          Team Rapid Recruits
          Kensie Cimo, Jenna Roberts, Haylee Armstrong, Insos Reza Febri Mayor, and Sam Straub

          2nd Place Tie, the Matchmakers
          2nd Place Tie

          The Matchmakers
          Natahn Hughes, Ally Galvan, and Drake Lowe

          Auburn Analysts, The People Choice Award
          People's Choice Award

          The Auburn Analysts
          Avery Foto, Austin Blackwell, Lauren Crimin, and Kendall Brewer

        • Fall 2023

          Fall 2023

          1st

          TEAM - Dream Team "Bussin’ it Down: Tiger Transit Analytics "

          First Place


          Team Members: Houston Prewett, Shaun Gutmann, Jiayi Wang, Abby Johnson, Alex Romer 

          2

          TEAM - Aerodynamic Analysts "Understanding safety and performance in f1 racing"

          Second Place

          Team Members:Hunter Lewis, Kelso Jacobson, Parker Elliot, Nelson Earley

          3

          TEAM - Skyline Chili Connoisseurs  "Bengals Defensive Analysis"

          Third Place

          Team Members: Cameron Binney, Tyler Morris, Hudson Van Allen 

          ppl

          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

           

          bapc20231stplacewinnersTEAM - Hunting Houses "Will the Bubble pop?"

          First Place


          Team Members: Kayla GallmanRebekah RathSage EllisHunter Langeand Evan Atkinson.

          2nd place

          TEAM - The Beatboxers  "Predicting COVID-19 Vaccine Breakthrough" 

          Second Place

          Team Members: Katie CrazeMegan GibsonBlake SpradlinAdam CamlicSamuel Burnette)

          3rd

          TEAM - The Roots  "Get to The Green: Analyzing Bonnie Plants Store Success"  

          Third Place

          Team Members: Heath VarmetteDax WilsonDavis HawkLandry StephensRob GriffinTucker Brant

          Pictured with data sponsor representatives from Bonnie Plants, Alayna Priebe and Elizabeth Henslee

          peopleschoice

          TEAM - Four Seam "Cold Pitches: The Effect of Weather on Baseball Metrics and Pitch Success"

          People's Choice Award

          Team Members: Alexa HannRapley HillsHannah HovenJosh PetramaleBoston SmithAidan 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

          Placeholder

          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).

          Placeholder

          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).

          Placeholder

          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 won first place award of $1,500 in May 2022s Poster Competition

          TEAM 8 - Auburn Analysts
          First Place
          Team members: Chloe Mikus, Isabel De Armas, Pierce Dickson, Brady Watts, and Anne Hays Wright

          Team 10 won second place award of $1000 in May 2022s Poster Competition

          TEAM 10 -Dill or No Dill
          Second Place
          Team members: Jack Ray, Danny Trainer, Kayla Taylor, Anthony Bostany, and Noah Vaughn

          Team 13 won third place award of $750 in May 2022s Poster Competition

          TEAM 13 - The Miner League
          Third Place
          Team Members: Frank Hudgins, Riley Spengeman, Mary Koch, Kyle Travelstead, Grace Crosson

          Team 7 won Peoples' Choice Award of $500 in May 2022

          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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        Pankush Kalgotra

        Pankush Kalgotra

        Assistant Professor

        Sumin Han

        Sumin Han

        Assistant Professor