This study explores the development of an AI Resume Analyzer, a tool designed to transform how resumes are evaluated by leveraging Natural Language Processing (NLP) and machine learning techniques. Despite the growing need for streamlined and unbiased resume analysis, many existing solutions lack personalized recommendations and actionable insights. The AI Resume Analyzer addresses this gap by automating resume parsing, extracting essential details like names, emails, and skills, and providing an evaluation score out of 10. Additionally, it offers constructive feedback and resource suggestions, including curated YouTube videos to help applicants enhance their resumes.

The research focuses on building a user-friendly interface using Streamlit, integrating Python modules such as pandas, pyresparser, and pdfminer3 for parsing and analysis, and employing Plotly for data visualization. A database system powered by MySQL ensures efficient data management and retrieval. The unit of analysis includes resumes processed through the tool, which are evaluated based on keyword matching, clustering into sectors, and overall presentation quality.

Findings from this study demonstrate that the AI Resume Analyzer significantly reduces manual effort while improving the accuracy and consistency of resume evaluations. However, challenges such as ensuring compatibility with diverse resume formats and continuously updating keyword databases remain areas for further development. These insights highlight the transformative potential of AI-driven tools in recruitment processes. Future research should focus on refining the analysis algorithms and expanding the system to accommodate multilingual resumes and domain-specific requirements.

Keywords— Resume analysis, natural language processing, Streamlit, recruitment automation, resume improvement, Python modules, keyword matching, AI in recruitment.