Employment dynamics: Exploring job roles, skills, and companies

Are you curious about which jobs are trending, which skills to learn for a better career, and which job is most relevant to your skills along with your probability of selection? This research article addresses these questions comprehensively. The first phase employs web scraping techniques to gather data from prominent job websites, constructing a current dataset. This dataset captures emerging job roles, in-demand skills, geographic job distributions, experience requirements, and related insights. In the subsequent phase, a job recommendation system is developed. Leveraging natural language processing (NLP) techniques such as term frequency–inverse document frequency (TF-IDF) vectorization and cosine similarity, the system matches user-provided skills with job descriptions to recommend relevant job opportunities. Additionally, the recommendation system estimates the probability of selection for each candidate. This project serves as a valuable resource for understanding contemporary job market dynamics, facilitating informed career decisions, and providing insights into emerging job trends and skill requirements crucial for success in the evolving job market.