CAREER COMPASS: AI-Based Resume Parser and Automated Job Recommendation System

There are many obstacles present in the current job application that candidates have to go through every day, including tedious work of copying the same data to inventories of resumes for every job, ineffective categorization of jobs, and complicated application processes. This paper proposes an ideal resume parser and job categorization solution. Python libraries are used to parse resume with the capability of extracting essential information from different format including PDF and Docx. This extracted data is fed to an intelligent job typification engine which enables job seekers to identify types of jobs suited to their aptitudes and inclinations. Data cleaning is done using regex approaches while vectorization was supported by tf-idf approaches. The evaluation of the system has been done using machine learning algorithms like Decision Tree, Random Forest, AdaBoost, SVM, & Logistic Regression. The performances have been compared on the basis of the training time, precision, accuracy, recall, and F1 score. To improve the efficiency of the system, it is synchronized with LinkedIn to extract real time jobs that are available for the probable job title. The primary goal of this approach is to reduce the time taken to engage in the application process, increase the degree of relevance of matching between applicants and vacancies, and in general, positively impacting the experience of the applicants in the job market.