Dynamic Pricing in Freelancing: An Approach to Fair Compensation for Freelancers

Stu-Lance is a new freelancing platform designed specifically for college students, using advanced machine learning to assure fair and transparent pricing. Stu-Lance uses a Random Forest, Adaboost Regressor and KNeighbours Regressor to dynamically estimate task prices based on deadlines, project duration, and service type. This research investigates Stu-Lance’s distinct strategy, which combines academic learning with real-world experience. We conduct a detailed evaluation of existing platforms and highlight important research needs, focusing on the constraints of current pricing structures. Our paper describes the dataset, preprocessing methods, and dynamic pricing model comparison using the above mentioned algorithms. Extensive exploratory data analysis illustrates Stu-Lance’s ability to ensure fair compensation and market relevance. This study highlights dynamic pricing’s potential to transform freelancing by encouraging efficiency, transparency, and fairness. Future work intends to improve model precision and explore blockchain integration for better pricing transactions.