Resume Analyzer using Natural Language Processing

Traditional hiring techniques are losing effectiveness as internet hiring grows more and more. It is not easy to manually filter out the resumes cause it would take a lot of time and resources, which the employing organizations cannot bear. Individuals with various specializations and sectors of experience submit a sizable number of unstructured resumes to job portals in a variety of styles and forms. Thus, to effectively channel candidates to their relevant occupational groups as well as facilitate the automatic screening of candidates, structured information must be extracted from application resumes. It's also unfair that many qualified candidates don't get the attention they deserve during the resume screening process. This might result in the hiring of incompetent people or the rejection of competent applicants. We present a method to address these problems by automatically recommending the most qualified job candidates in accordance with the provided job description. Our solution employs NLP to pull relevant information from the unstructured resumes, such as skills, education, and experience, and then summarizes each application. Using the spaCy library in python and the modules such as Entity Recognition, Dependency parsing & LDA (Latent Dirichlet Allocation) for topic modelling.