Roomifyer: Vector-Based Roommate Matching Platform Roomifyer is a data-driven decision support system designed to match potential roommates based on lifestyle compatibility and budget constraints. Unlike traditional search-based platforms, Roomifyer utilizes mathematical metrics to predict the most accurate matches between users.

🚀 Overview Finding a compatible roommate is a complex multi-dimensional problem. Roomifyer solves this by analyzing 12 distinct lifestyle parameters and financial data to provide a "Compatibility Score." This project served as my Senior Graduation Project at Ege University.

🛠 Tech Stack Frontend: React.js + Vite (for high-performance development and optimized builds).

Backend & Database: Firebase (Real-time Database & Authentication).

Styling: Modern CSS/SCSS with a focus on dark-mode UI/UX.

🧠 Technical Depth: Vectorial Decision Support The core of Roomifyer lies in its matching engine. The system transforms qualitative interview data into quantitative vectors:

12-Parameter Lifestyle Analysis: Users are evaluated on habits like smoking, social drinking, and daily routines.

Euclidean Distance Metric: The algorithm calculates the geometric distance between user vectors in a multi-dimensional space.

Budget Assimilation: Financial constraints are assimilated into the user's vector to refine the matching accuracy.

Match Prediction: The system outputs a percentage-based compatibility score (e.g., %59 Match) to help users make informed decisions.

✨ Key Features Dynamic Interview System: Collects structured data from users to build their "Lifestyle Profile."

Real-time Matching: Instant calculation of compatibility scores as new users join the platform.

Professional UI: A sleek, user-friendly interface featuring detailed profile cards and decision-support panels.