ITDS | SPMS

Curriculum Vitae Analysis And Ranking System Using Machine Learning For Human Resources

Project Description
Supervisor

The boost in job applications has increasingly become a challenging duty for Human Resource departments over many organizations. As the number of applicants increases, it becomes more and more challenging to effectively filter through CVs and make sure that every candidate is assess fairly and thoroughly. Let's face it, manually sorting through job applications can be a nightmare. It's slow, and sometimes, unconscious biases can slip in. We wanted to fix that. So, we are to build a system that uses AI to scan resumes and make the hiring process smoother. No more tedious reviewing, and no more unfair advantages. Just a faster, fairer way to find the best candidates. We've developed a hiring system that's both precise and fair. At its core is a well-established prediction tool. To refine its accuracy, we've added language understanding capabilities. This allows our system to review resumes thoroughly, considering essential qualifications like education and credentials, technical expertise, interpersonal skills, and professional background. Based on these factors, our system streamlines the hiring process and helps us make informed decisions. So, how did we build our hiring system? Well, this document walks you through the whole process. First, we'll cover how we designed the system and its architecture. Then, we'll dive into how we trained our machine learning model to make smart predictions. You'll also learn how Natural Language Processing (NLP) helps us understand resumes better. Finally, we'll explain how we measure the system's performance to ensure it's doing its job: making recruitment more accurate and fair. The results of this project underscore the potential impact of such a system on HR operations. Doing this will significantly reduce the need for manual CV reviews, it alleviates much of the administrative burden traditionally faced by HR personnel. In addition, the system’s ability to fairly assess candidates based on a wide qualities of qualifications ensures that all applicants are given equal consideration, regardless of factors that might otherwise introduce bias. The system could be expanded to incorporate more advanced machine learning techniques, potentially increasing its accuracy and versatility. Moreover, further research could explore integrating additional data sources or refining the criteria for evaluation, enabling even more precise and tailored assessments of candidate suitability

Machine Learning: Machine Learning (ML) research in Computer Science and Information Technology focuses on the development of algorithms and models that enable computers to learn from data and improve their performance over time without being explicitly programmed. It is a subset of Artificial Intelligence that uses statistical techniques to give machines the ability to learn patterns, make decisions, and predict outcomes based on data. Supervised learning, a key area of ML research, involves training models on labeled data, where the input-output relationships are predefined. This method is widely used for tasks such as classification (e.g., spam detection) and regression (e.g., predicting house prices). Unsupervised learning, on the other hand, involves finding hidden patterns in data without predefined labels, with clustering and association being typical applications in areas such as customer segmentation and anomaly detection. Reinforcement learning is another area of ML that focuses on teaching agents to make decisions by interacting with their environment and receiving feedback in the form of rewards or penalties. It is often applied in robotics, game playing, and autonomous systems, where continuous learning and adaptation are required.

The primary objective of this project is to develop a machine learning-based CV analysis and ranking system that enhances the efficiency, accuracy, and fairness of recruitment processes.

2023/2024

Nov 18, 2024

HARUNA B. ABDUL-RASHID SEIDU MUNTARI SULLEY NOBLE REXFORD ANNAN PROSPER KYEREMEH ABDALLAH MOHAMMED

BSc Information Technology