| Abstract: | 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. |
