Job Recommendation System for Daily Paid Workers using Machine Learning

Abstract

Abstract: In developing countries where unskilled workers often face challenges in finding suitable employment, this is where recommendation systems come in, as they can help users find information that is specific to their interests. Using the system decision-making and predictions through algorithms trained on available data across multiple domains makes it easy for users to access pertinent information. One area where recommendation systems can have a significant impact is in helping unskilled workers find jobs based on their skills and interests. Although there are many jobs available for skilled professionals, it can be challenging for daily wage workers to find suitable employment due to a lack of information and awareness. Currently, there are no relevant recommendation systems available to help these workers. In this research, we propose a "Job recommendation system for daily paid workers using Machine Learning" that analyzes a worker’s skills and interests to find appropriate job opportunities. To ensure that the system is robust, we consider a wide range of factors when recommending jobs to daily wage workers.