Contributors :-

Mentors :-

Tech stack:-

• Python
• Streamlit
• PyPDF2,PyMuPDF
• Spacy
• TF-IDF(Term Frequency-Inverse Document Frequency)
• Git and GitHub

Components Used:-

1. Natural Language Processing (NLP)
2. Machine Learning
3. Text Extraction
4. AI
5. Python

Introduction  :-

The aim of this project is to develop a Resume Analyzer System that, unlike traditional resume screening methods, automatically extracts, analyzes, and ranks resumes based on job descriptions. It identifies key skills, experience, and education using NLP techniques, calculates job match scores, and provides ranked results. The system is built using Python, Streamlit, and various NLP libraries, with extracted results available for download in CSV and PDF formats.

Methodology :-

1. Learning and understanding Python and its libraries such as PyPDF2, PyMuPDF, and spaCy for text extraction and NLP processing.
2. Building a program to extract and preprocess resume content, including tokenization and lemmatization.
3. Developing algorithms for skill extraction, experience detection, and education classification.
4. Implementing job match scoring using TF-IDF similarity and ranking resumes based on technical and managerial criteria.
5. Generating downloadable reports in CSV and PDF formats for easy review.

Description :- 

The Resume Analyzer System is an advanced AI-powered solution designed to automate resume screening and ranking. Combining natural language processing with machine learning, this system efficiently extracts key information from resumes, evaluates job relevance, and generates structured insights. It streamlines the hiring process by providing accurate resume analysis and ranking based on job descriptions.

Features:

1. Resume Parsing: Extracts text from resumes in PDF format using PyPDF2 and PyMuPDF.

2. Skill Extraction: Identifies key technical and managerial skills using NLP techniques.

3. Experience & Education Detection: Analyzes years of experience and determines education level.

4. Job Match Scoring: Uses TF-IDF similarity to compare resumes with job descriptions and rank them.

5. Resume Quality Analysis: Evaluates structure, completeness, and readability for better insights.

6. Automated Ranking: Assigns scores based on technical, managerial, and overall job match criteria.

7. Downloadable Reports: Provides analyzed results in CSV format and top-ranked resumes as PDFs.

Benefits:-

• Reduces manual effort in evaluating resumes, saving time for recruiters.
• Quickly identifies the most relevant candidates based on job descriptions.
• Provides structured insights and rankings to support better hiring choices.
• Uses standardized NLP techniques to analyze resumes objectively.
• Generates reports that help in optimizing hiring strategies.

Real-life applications :-

1.  Automated Resume Screening for HR departments to filter and rank candidates efficiently.

2.  Talent Acquisition Platforms to enhance applicant tracking systems with AI-driven analysis.

3.  University Career Services to help students optimize their resumes for job applications.

4.  Freelance Marketplaces to match freelancers with job postings based on skills and experience.

5.  Corporate Hiring Solutions to streamline large-scale recruitment processes.

6. Government Job Portals to ensure fair and efficient candidate evaluation.

7. Consulting Firms to analyze candidate profiles for specialized roles.

8. Remote Hiring Platforms to evaluate global talent without manual intervention.

9. Job Matching Services to provide AI-powered recommendations for job seekers.

Problem faced :-

1. Installing and configuring NLP libraries like spaCy and PyMuPDF for resume parsing.
2. Extracting structured information from unstructured resumes with varying formats.
3. Handling different file types (PDF, DOCX) and ensuring accurate text extraction.
4. Optimizing job description matching using TF-IDF and similarity scoring.
5. Integrating parsed data into a user-friendly GUI with Streamlit.
6. Managing large datasets efficiently to maintain fast processing times.
7. Ensuring accurate skill extraction and categorization across multiple industries.
8. Deploying the application with seamless functionality and minimal dependencies.

                                               Thank you, 

                                                                                        Team Resume Analyzer