Abstract:
One of the most significant and critical tasks for every firm is to find the right person for
the position. As online recruitment becomes more prominent, conventional hiring practices
are becoming inefficient. Conventional approaches typically consume more time due to
manually reviewing all applicants, assessing their resumes, and then creating a list of
candidates who should've been interviewed. Many company hires other firms to screen
their candidates resume and find out the suitable person for the position. In this information
age, job searching has become both smarter and easier. Companies get a lot of
resumes/CVs, and many of them aren't well-structured. Finding suitable candidate for any
position takes a significant amount of time and effort. In this study, we have come up with
an easy and effective solution for this tedious work. We build three models KNN, Random
Forest Classifier and DistilBERT on same dataset for resume classification process. KNN
and Random Forest Classifier model have achieved highest accuracy 98% among all the
models.
