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The document classification solution should significantly reduce the manual human effort in the HRM. It should achieve a higher level of accuracy and automation with minimal human intervention.
This project is designed to classify resumes into different job categories based on their content. Users can upload resumes in PDF, DOCX, or TXT format, and the application will predict the category using a pre-trained machine learning model. It aims to enhance the job application process for candidates and recruiters alike.
🚀 AI-powered Resume Classifier using NLP and SVM. Automates candidate screening by categorizing PDF/Docx resumes into technical roles with an interactive Streamlit dashboard.
SKILLSCAN AI is an AI-powered resume classification tool that leverages NLP, TF-IDF, and machine learning models to categorize resumes into job roles with high accuracy.
resume classifier that analyzes input resumes and provides a similarity score based on a given job description (JD). This classifier will be designed to determine how closely a resume matches the requirements and qualifications specified in the JD.
Resume classification is the task that automatically categorizes resumes or CVs into predefined domain categories or classes based on their content. This task is essential for the job recruitment process, particularly when organizations receive a large number of applications for various positions.
The document classification solution should significantly reduce the manual human effort in the HRM. It should achieve a higher level of accuracy and automation with minimal human intervention.
This resume classification uses the Naïve Bayes algorithm to classify resumes into specific divisions to assist the Human Resources (HR) team in the candidate selection process.
A Machine Learning project for classifying resumes into categories to streamline resume sorting and enhance recruitment processes
A text classification model from data collection, model training, and deployment.
NLP pipeline to classify resumes using TF-IDF, Word2Vec and KNN - 85% reduction in manual screening time. Built with Python and Streamlit.
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