RESUMECRAFT – BEST RESUME BUILDER AND SCREENER USING NLP AND ML

Abstract

This paper presents ResumeCraft, a web-based application designed to assist users in creating, editing, and screening resumes using Natural Language Processing (NLP) and Machine Learning (ML) techniques. The system provides an integrated platform that simplifies the resume development process while enhancing its effectiveness for job applications. It begins with a user-friendly landing interface that guides individuals through the various functionalities of the system. The application includes a built-in resume editor with customizable templates, enabling users to design and modify resumes in a structured and visually appealing format, similar to modern design tools. Users can easily generate, update, and download resumes in multiple formats suitable for professional use. In addition to resume creation, the system incorporates a keyword recommendation module that suggests role-specific skills and terms based on industry requirements. This feature helps users improve resume quality and ensures better compatibility with Applicant Tracking Systems (ATS), which are widely used for automated resume filtering. The proposed system also includes a resume screening component that analyzes uploaded resumes and predicts suitable job roles using machine learning models trained on textual data. The system applies preprocessing techniques such as text cleaning, tokenization, and feature extraction using TF-IDF to improve classification accuracy and relevance. Furthermore, the application provides real-time feedback to users, allowing them to refine their resumes based on predicted outcomes and keyword suggestions. By combining resume building, keyword optimization, and automated screening into a single platform, ResumeCraft offers a comprehensive solution for job seekers. The system improves resume quality, increases job matching accuracy, and supports efficient career development processes through intelligent automation techniques. The system is designed to be scalable and adaptable across multiple domains, allowing users from different professional backgrounds to benefit from its features. It also enhances usability by providing a seamless and intuitive experience for both beginners and experienced users.

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