Machine learning coursework
Dating-Website Application
A graduate team project focused on designing and evaluating AI-assisted compatibility matching for a dating application.
Interactive preview
Try a demo
Explore a matching illustration. This is a small browser-only illustration, not the full application. AI responses are scripted; model and desktop features are not running here. Inputs stay on this page and reset when you reload.
Result
Ready. Change the sample and run the demo.
Explore the real workflow
Define the matching question and available profile signals.
The full app's capabilities and requirements are described below.
What it is for
A graduate team project focused on designing and evaluating AI-assisted compatibility matching for a dating application.
Compatibility matching is a useful case study in translating subjective preferences into measurable signals while confronting uncertainty, bias, privacy, and the limits of an algorithmic recommendation.
- Study matching design
- Translate profile and preference signals into a compatibility experiment.
- Evaluate recommendations
- Discuss how model choices affect uncertainty, fairness, and ranking.
- Review a team case study
- Understand the coursework scope without mistaking it for a public dating service.
Get started
Read the project goals, matching workflow, and limitations below. There is no public app to sign up for or download at this time.
- This page is a coursework case study, not a downloadable service.
- No public repository, installer, or live demo is currently listed.
- Any future real-data evaluation would need consent and appropriate privacy controls.
Features and workflow
- Matching design
- Translate profile and preference information into candidate compatibility features.
- Model evaluation
- Compare matching behavior using course-defined machine-learning methods and evaluation criteria.
- Team delivery
- Divide research, modeling, application design, and presentation work across a graduate project team.
- Responsible framing
- Treat a score as decision support rather than a claim that compatibility can be known with certainty.
- Define the matching question and available profile signals.
- Prepare features and candidate-pair examples.
- Train or apply the selected matching approach.
- Evaluate the results and discuss limitations.
- Present the prototype and findings as a course deliverable.
Architecture
The coursework prototype centers on Python-based machine-learning logic and an application concept rather than a hardened production service. Its value is the matching experiment and evaluation process.
Privacy and limitations
Dating data is sensitive, and matching systems can amplify bias or create false confidence. A production version would require clear consent, data minimization, safety controls, fairness testing, and user control over recommendations.
Status
Completed as a DePaul “Machine Learning for Managers” team project. There is currently no public repository or live deployment, so no Try Now link is shown.
- Availability
- Coursework only
- Type
- Team project
- Focus
- Compatibility matching
- Public demo
- Not available
Questions
Can I try the dating app?
There is no public live deployment or repository listed for this coursework project.
Does a score establish compatibility?
No. A matching score is uncertain decision support and cannot establish a relationship outcome.
Why show this project here?
It documents the design and evaluation work from a graduate team project, including the limitations of applying machine learning to sensitive preferences.