Resume Skill Extraction and Improvement Suggestion Tool

Added Jun 2025 3 design docs

Most resumes are rejected in seconds, often by screening software, and job seekers rarely learn why. Career centers and bootcamps need scalable ways to give students concrete, individualized resume feedback instead of the same generic checklist, and students need to see exactly which skills their documents do and do not communicate.

The intern builds a Flask web app in Python where users upload a resume and machine learning does the review. The pipeline extracts text from uploaded files, applies natural language processing to identify skills, tools, and experience signals, and compares the resulting profile against patterns for the user's target roles. The app highlights detected strengths, flags missing keywords and weak sections, and suggests specific improvements, presenting everything in a clear before-and-after style. The intern builds the text-processing pipeline with standard data science libraries and deliberately designs the scoring logic to be transparent rather than a black box.

The project teaches practical text analytics, from file parsing and feature extraction on unstructured documents to explainable scoring, and delivers a portfolio piece with obvious real-world value to any student audience.

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