THE HACKATHON EXPRESS BY THE UX FACTOR

LINK TO ACCESS PRESENTATION:

(Normal Link)

https://www.canva.com/design/DAGhJ1aoi6M/59x47YbW8Ss3NLSmw4eklA/edit?utm_content=DAGhJ1aoi6M&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton

Team Roles and Contributions

Smita Bajaj

• Integration of AI model using python/tensorflow

• Streamlit App to predict candidate attendance.

Angel Nwankwo

Angel Nwankwo contributed to the back end development of the project.

Anastasia Florentis

Anastasia Florentis played a key role in the UX design of the project using FIGMA.

Dimitrina Grazhdani

Dimitrina Grazhdani focused on graphic design elements for the project.

Bianca Gutu

Bianca Gutu focused on graphic design elements for the project and user research.

Framing the Problem

What is the problem you are trying to solve? Who does it affect?

Hackathons and events often face high no-show rates (20–50%), making it difficult to allocate resources, form balanced teams, and drive engagement. Unpredictable attendance disrupts logistics, while a lack of pre-event interaction increases uncertainty, anxiety, and dropouts. This widespread issue affects organizers, attendees, and sponsors alike.

Idea Explanation

What is your idea? How does it fix the problem?

Our solution tackles hackathon no-show rates by making pre-event engagement fun and reducing uncertainty. We introduced a Party Train on the event website, where attendees are placed in virtual train carriages upon signing up. Each carriage represents different engagement levels leading up to the event, allowing participants to see who else is attending, color-coded by specialty and experience to make new hackers feel more comfortable. Attendees can play mini "party games" to move forward in the train, fostering interaction and excitement while giving organizers insight into engagement levels. Reaching the end of the train unlocks opportunities to connect with sponsors and organizers. Additionally, we built an AI model to predict attendance likelihood, enabling organizers to proactively engage at-risk attendees and minimize last-minute dropouts. This two-pronged approach makes hackathons feel more welcoming, builds community, and improves event planning efficiency.

Implementation

How do all the pieces fit together?

1. UX Design: Figma UI flow

   The Figma UI maps out the user journey from registration to ticket download.

2. User Experience Map

   Visual representation of how users interact with the app at each stage.

3. PowerPoint Presentation

   Showcases the Figma UI flow and the overall experience.

   Includes screenshots, architecture diagrams, and a video.

4. Backend :

   Handles data processing and AI model inference.

   Receives user data, scales inputs, and predicts attendance probability.

5. AI Model Integration:

   A TensorFlow/Keras model predicts whether the candidate will attend or not.

   It learns from user progress (levels completed) and other engagement factors.

Challenges

What did you struggle with? How did you overcome it?

• AI Model: Lack of a Real Dataset to train a model.
  Solution: Synthetic Data Generation Created a mock dataset with realistic patterns using Python (pandas & numpy).

• Time management: we overcame the constraints of time by simplifying our problem and focusing on the reasons why it would be effective.

• Learning new languages to boost the complexity of the project

• UnsplashAPI no longer works and you had to pay for openai, hence couldn't make calls to implement AI in the solution.

Accomplishments

What did you learn? What did you accomplish?

• As a group of tech-driven women, we embraced collaboration, innovation, and problem-solving under pressure. Through this hackathon, we explored new technologies, refined our coding and design skills, and gained hands-on experience in transforming ideas into real solutions. More than anything, we learned the value of resilience and adaptability in overcoming challenges. We also recognized how complex it is to rely on people, making this a particularly difficult problem to tackle. However, we believe our solution will help ease common anxieties, foster a sense of community, and give organizers valuable insights into attendee engagement leading up to the event. By leveraging diverse perspectives and skill sets, we ensured our idea balanced both innovation and impact. Open discussions and iterative decision-making allowed every voice to be heard, leading to a well-rounded, thoughtful solution. Our teamwork, technical execution, and problem-solving mindset demonstrate how women in tech can drive meaningful change when we collaborate, support, and uplift one another.

Next Steps

Idea 1 : Gamified Sponsor Train

To tackle high no-show rates, we introduce a "Sponsor Train", a gamified system where sponsors actively compete, much like participants. Sponsors earn visibility, perks, or rewards based on their engagement and contributions, encouraging more sponsorships and funding.

Idea 2 : Globalise Model

Globalise the model by creating a "map" of stations around the world with different ongoing hackathons - allows people to engage on a global scale. Additionally, online hackathon's would also be shown.

Idea 3 : Invest In Marketing

Cater to a younger audience and play an exclusivity element to the prototype.

Idea 4: The Predictability Rails

Are a prototype which could be integrated into wider data-sets, instead of checking one by one, organisers would be able to produce visuals such as graphs ext...