Extraction of skill sets from unstructured documents

Automated resume classification refers to the use of advanced technologies, such as deep learning models and ensemble learning approaches, to analyze and categorize resumes automatically. This process is crucial for efficiently handling the overwhelming volume of job applications that organizations receive. One prominent method for automated resume classification involves utilizing Distil-BERT, a state-of-the-art deep learning model specifically designed for natural language processing tasks. DistilBERT processes textual information from resumes, extracting meaningful patterns and features to make informed predictions. Additionally, an ensemble learning approach involves combining various classifiers that are trained independently, and their predictions are aggregated to produce a final classification decision. By leveraging both DistilBERT and ensemble learning, organizations can streamline their recruitment process, save time, and improve the accuracy of resume screening, ultimately leading to more effective talent acquisition. The best accuracy score obtained by DistilBERT is 94.16%. and The Ensemble learning model’s accuracy score is 86.83%.