Predictive Analytics for Freelancer Compensation: A Modeling Approach

This study focussed on freelancers, particularly in the delivery sector, where delivery-based wages are common. This analysis interprets and provides an accurate amount of wages for the freelancers and embeds mathematical modeling for calculating hourly wages with Non-linear transformations, hybrid modeling for parameters and representing graphical methods, and comprising the data of the real-time scenario in the prevailing job market. The Embedding Machine Learning model features a Random Forest Algorithm which predicts the hourly wages of each employee and controls overfitting. The proposed solution provides an efficient wage prediction and obtains a high performance. Furthermore, providing the model, is user-friendly, which allows employees to input their performance metrics, and obtain real-time predictions of hourly wages. Real-time tracking tools help ensure hourly work, providing actionable knowledge for workers and businesses and data-driven insights to enhance worker experiences. Furthermore, the bridge gap between the freelancers and the job market includes, providing hourly wages for freelancers in the on-demand service gig economy, providing advanced search functionality using Artificial Intelligence in the Machine learning model, Time tracking, embarking comprehensive portfolios, and profiles will enhance visibility and trust between freelancers and employers. The intercession aims to develop a gig economy ecosystem, bridging existing gaps and strengthening long-term sustainability in the freelancer’s community. This research concludes that providing per hourly wages, for each freelancer and increasing the sustainable gig economy in the community.