Abstract:
Resume Screening is a necessary step in the process of hiring new employees.
Overloading of applications makes job selection process a strenuous task. One of the
critical decisions is to select resumes which fit to the Task – Talent, Person – Position
and Individual – Institution. Since, Employers have to do this manually for each
individual’s resume, it is rather laborious and time-consuming. And also resumes are
in unstructured different formats. It is very stressful to go through hundreds of resumes.
To do it without any bias and to speed up the process Automated Resume Screening
and Personality Prediction system is proposed.
To provide a solution for the problem, it divided into two components. First, to identify
and calculate skill wise similarities with the job requirement and then, predict the
personality of the candidates. In this study, author identifies resume screening
problems, issues in existing systems and Machine Learning algorithms. After analyzing
above factors and dataset, Author decided to implement ensemble model to predict
personality of the candidates.
Therefore, unstructured resume data were used for the skill wise similarity calculation
and personality prediction. To predict personality of the candidate’s ensemble model
was developed using Naive Bayes, Logistic Regression, Support Vector Machine,
Random forest algorithms and XgBoost and LightGBM models. The overall feedback
of the Resume Screening and Personality Prediction System from technical and domain
experts was positive.
