Project Matchmaker

ECE4424/CS4824: Machine Learning

Fictional teaching profiles: all names, contacts, responses and feature values are invented. No class roster is used.

Find ML project partners using k-nearest neighbors. This tool matches students with similar interests and complementary skills while demonstrating practical applications of k-NN algorithms.

Select any two dimensions to explore different aspects of compatibility. Adjust k to see more or fewer potential teammates.

Fictional profile email (optional):

Dimension Selection

X-Axis:

Y-Axis:

Color By Category: Number of Nearest Neighbors (k): 3

Add jitter to spread overlapping points

Jitter amount: 0.05

Visualization

Selected Student

Click on a student in the visualization to see details

Nearest Neighbors

Select a student to see their nearest neighbors

Developed by Prof. Ming Jin for ECE4424/CS4824: Machine Learning.

All profiles, responses and numerical features are fictional teaching examples. No personal records are included.