Resume Classification Using ML Techniques

Abstract

In today’s world, a typical job ad on the web attracts a massive number of applications in a short period of time. Manual screening of these resumes is not only time-consuming but also very expensive for the hiring companies. To address these challenges, this research paper proposes a solution that aims to automatically classify resumes to their corresponding suitable positions. To find the best possible solution, different ML techniques like Decision Tree, Random Forest, KNN, Support Vector are researched and the most accurate one is chosen. This approach has the potential to revolutionize the hiring process by reducing costs, saving time, and ensuring fairness.