Parth Dhama

Using a linear optimization algorithm, I created a Python library to perform mentor-student matchmaking. Once the library is installed, the developer can add classes of users against a given interface that can define multiple attributes such as availability, skills, and preferred skills. These attributes can also be classified into hard requirements, like availability, or soft requirements, like preferred skills. Then a single function call can perform matchmaking across two sets of given users.

The main advantage of this library is that it allows to define any number of user attributes against which matching needs to happen. It also specifies the respective weight of each attribute, determining how important it is dynamically. Another significant advantage of the library is that if no possible solution exists for given constraints and their weights, suggestions are made available to modify the attributes for some users to make the match possible.