Resume Screening App Using Machine Learning

High velocities are associated with the recruitment processes of current hiring environments, and sifting through large volumes of applications pose challenges. With traditional resume screening being labor-intensive, time- consuming, and prone to human error, it translates into delays in recruitment and failure to identify some candidates that might fit the requirements of a post. This paper outlines an automated resume-screening application aimed at fastening the recruitment process through automatic resume categorization according to professional fields. The proposed system is based on ML and NLP techniques, with the application to be designed to classify, from an uploaded resume, the relevant field of the uploaded resume-for instance, information technology, marketing, finance, healthcare, and more. The application was developed using Python for both the machine learning and user interface components. The development environment was Jupyter Notebook. NLP techniques are used to extract key features from the resume text, and a classification model is trained to predict the most relevant field for each document. It used a dataset with resumes from various fields and obtained a high accuracy rate for the correct indication of the resume's field, thereby proving the correctness and dependability of the system.