We have now entered the job searching pool and have discovered many difficulties faced by both employers and applicants. We wanted to make a project which would help both the employers and the applicants by using data technologies to overcome inefficient job screening.
What it does
Our project uses natural language processing to find a match between a job description and a resume by not only using direct keyword matching but also matching words closely related yet relevant to the job.
How I built
We used Python to write functions for vector similarity to create an annotated dataset for our use. We leveraged that data set to train a model which was then able to give a matching percentage between a resume and a job description
Challenges I ran into
Accomplishments that I'm proud of
We are proud of the accuracy our algorithm was able to achieve. We are also happy with the test dataset we were able to create.
What I learned
We learned a lot about natural language processing, vector similarities, and building GUIs
What's next for SkillSpell
We would aim to achieve much higher accuracy from our algorithm for resume screening by getting better datasets and pre-processing it more efficiently.