AI-Enhanced Recruitment Systems: Transforming Online Hiring Processes with Intelligent Automation for Improved Talent Acquisition and Efficiency

AI-Enhanced Employment Systems: With Intelligent Robotics for Improved Acquisition of Talent and Efficiency — As talent acquisition trends evolve and the recruitment landscape changes, the demand to sustain the online hiring process is real. This paper proposes a new framework that incorporates machine learning techniques i.e. TF-IDF (Term Frequency Inverse Document Frequency) & KNN classifier to enhance and automate the assessment of resumes across multiple domains like analytics, data science, machine learning and Java. The system has a comprehensive data preprocessing technology to ensure the data quality, and finally the efficient text vector processing and feature extraction implemented by TF-IDF. KNN ranks resumes relevant to designer and subject matter experts. It has shown significant accuracy of 89% on the trainer data and 82% on the test data. Word cloud image helps to visualize word distribution, and filtered phrases, competencies contribute to analysing the data, while simple streamlit UI makes it easy to upload resumes and analyse them. In addition to offering holistic ratings by domain, it offers tactical advice about whether to code-match or not-code match someone based on their coding criteria. The benefit of this strategy is that it is a useful tool for the modern HR process and helps improve the objectivity of hiring and the efficiency of decision-making while also speeding up the hiring process.