Recrute Manager

Résumé: This thesis presents the development and implementation of an innovative web-based platform designed to streamline and enhance the job recruitment process. The platform leverages the power of machine learning to automate the extraction of crucial information from job applicants' resumes, significantly reducing manual effort and human error in candidate assessment. The study begins with a comprehensive analysis of existing recruitment procedures and identifies the challenges and inefficiencies faced by organizations. Subsequently, we delve into the architecture and design of our web application, highlighting its user-friendly interface and integration capabilities with various data sources. The core innovation lies in the utilization of machine learning algorithms, specifically natural language processing techniques, to accurately and efficiently extract essential data points from resumes. This automated parsing not only saves valuable time but also ensures consistency and objectivity in candidate evaluation. Through extensive experimentation and evaluation, we demonstrate the effectiveness of our system in terms of data accuracy, processing speed, and overall efficiency. Real-world case studies and user feedback validate the practicality and usability of the platform in diverse recruitment scenarios. In conclusion, this thesis presents a robust solution to the challenges of modern recruitment by harnessing the capabilities of machine learning and web technology. The developed platform offers organizations a competitive advantage in identifying the most qualified candidates, ultimately contributing to improved hiring decisions and organizational success.

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