Abstract
Abstract: The competitive job market necessitates well-crafted resumes that resonate with both human recruiters and Applicant Tracking Systems (ATS). This paper introduces ResumeCraft, a web-based platform empowering users to build strong resumes and optimize them for ATS compatibility. ResumeCraft leverages Machine Learning (ML) for data analysis and user guidance, while the user interface is built with Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript for a user-friendly experience. The system allows users to input their personal and professional details through a series of form fields, and provides a real-time preview of the resume design as the user inputs their data. The resume generator uses JavaScript to dynamically populate the preview with the user's input, and allows users to select from a range of pre-designed templates and color schemes to customize the look and feel of their resume. It processes the user input and generates a downloadable Portable Document Format (PDF) of the final resume. The platform analyzes user-provided information through ML models, offering suggestions on skill extraction, keyword matching, and action verb usage. This combination empowers users to create impactful resumes that are more likely to pass through ATS filters and reach human reviewers.
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