study group matching service

Group matching service using cosine similarity, agglomerative clustering, Multi Dimensional Scaling(MDS).

Used library

• numpy
• sklearn (cosine_similarity, AgglomerativeClustering, MDS)
• json
• matplotlib

Code

algorithm.py : Main code, here we calculate the cosine similarity, agglomerative clustering, Multi Dimensional Scaling(MDS).

show_clustering.py : Visualize clustered data with matplotlib using Algorithm.py's function

show_matrix.py : Visualize matrix that transforms a cosine similarity based matrix into a distance matrix

students_data.json : Json file of student’s information including vectorized data

vector.py : Preprocessing and vectorize the data from user. Values can be directly entered at the main function and save the data as a json file.