ResumeMaster — AI Resume Screening & Job Role Recommendation System

An AI-powered web application that analyzes resumes, predicts suitable job roles, performs skill gap analysis, detects experience level, identifies spelling mistakes, provides resume improvement suggestions, and includes a chatbot assistant for guidance.

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🎯 Objective

Design and develop an end-to-end AI-based Resume Screening System with a Chatbot Assistant that:

• Automatically analyzes resumes and predicts suitable job roles
• Provides actionable insights for resume improvement
• Assists users interactively through an AI-powered chatbot

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❗ Problem Statement

Manual resume screening and candidate guidance are:

• Time-consuming and inconsistent
• Error-prone for recruiters
• Lacking in actionable feedback for applicants, such as:
  • Skill gaps
  • Role alignment
  • Resume quality issues
  • Experience level detection
  • Guidance through questions via chatbot

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💡 Proposed Solution

ResumeMaster provides an intelligent platform that:

• Accepts resumes in PDF, DOCX, and TXT formats
• Extracts and cleans resume text using NLP
• Predicts the most suitable job role using TF-IDF + Logistic Regression
• Shows Top 3 role recommendations with confidence scores
• Matches candidate skills with job requirements and highlights missing skills
• Detects spelling mistakes and suggests corrections
• Identifies experience level (Fresher / Junior / Mid / Senior)
• Provides a chatbot assistant for resume and career guidance

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🔄 System Workflow

1. Resume Screening:

   • Upload resume → Extract text → Preprocess → Convert to TF-IDF → Predict job role → Generate:
     • Predicted job role
     • Top 3 role recommendations
     • Matched & missing skills
     • Spelling mistakes and suggestions
     • Experience level classification
     • Resume improvement suggestions

2. Chatbot Assistance:

   • User interacts with chatbot
   • Chatbot answers questions on resume improvement, skill building, career guidance
   • Powered by AI with NLP and retrieval-augmented knowledge

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🧠 Machine Learning & NLP Techniques

Resume Screening:

• Text preprocessing (regex cleaning, normalization)
• TF-IDF Vectorization
• Logistic Regression classifier
• Skill extraction via keyword matching
• Spelling correction using TextBlob or SymSpell
• Optional NLP improvements using NLTK or spaCy

Chatbot:

• Large Language Model (LLM) / GPT-style
• Retrieval-Augmented Generation (RAG) for real-time guidance
• Vector embeddings stored in FAISS or similar for semantic search
• Context-aware responses and multi-turn conversations

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🛠️ Tech Stack

Backend:

• Python
• Flask (web app)
• scikit-learn
• NLTK / TextBlob / spaCy
• PDFMiner / pdfplumber (PDF parsing)
• python-docx (DOCX parsing)
• Pandas & NumPy

Frontend:

• HTML, CSS, JavaScript
• Bootstrap or TailwindCSS for styling

Chatbot:

• Python (Flask API)
• FAISS vector database
• OpenAI GPT / LLM integration for query responses

Other Tools:

• Git & GitHub for version control
• Optional: pdfkit for generating downloadable PDF reports

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⭐ Key Features

• Multi-format resume upload (PDF, DOCX, TXT)
• Automated resume text extraction & cleaning
• Job role prediction (Logistic Regression)
• Top 3 role recommendations with confidence scores
• Matched & missing skills (skill gap analysis)
• Experience level detection
• Spelling mistake detection & suggestions
• Resume improvement tips
• Chatbot assistant for guidance and Q&A
• Downloadable resume analysis report (PDF)

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📊 Expected Output

• Predicted job role
• Top 3 role recommendations with confidence
• Matched & missing skills
• Experience level classification
• Spelling mistakes and suggestions
• Resume improvement insights
• Interactive chatbot assistance

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🚀 Quick Start — Clone & Run

1. Clone the repository:

git clone https://github.com/Ankitaghavate/ResumeMaster-ATS.git
cd ResumeMaster-ATS

2. Create a virtual environment (recommended)

python -m venv venv
# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Run the Flask application

python app.py

5. Open in your browser

http://127.0.0.1:5000/

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🌐 Live Demo

A demo may be available at: https://resume-master--ank26.replit.app | https://resumemaster-ats-1.onrender.com

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📦 Files of Interest

• app.py — Flask application entrypoint
• best_model.pkl — Trained Logistic Regression model
• tfidf.pkl — TF-IDF vectorizer used for feature extraction
• label_encoder.pkl — Label encoder for job roles
• AI_Resume_Screening.ipynb — Notebook used for data exploration and model training
• UpdatedResumeDataSet.csv — Dataset used for training

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📝 Notes & Recommendations

• Ensure the pretrained model and vectorizer files (best_model.pkl, tfidf.pkl, label_encoder.pkl) are present in the repo root.
• For production deployment, consider:
  • Using a proper WSGI server (Gunicorn / uWSGI)
  • Packaging models or loading them from secure storage
  • Adding rate-limiting and authentication for the API
  • Improving parsing for complex resume formats (tables, images)
  • Using a more advanced spell-checker or contextual model for suggestions

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🤝 Contributing

Contributions are welcome! Please open issues or pull requests for bug fixes, improvements, or feature requests.

Suggested workflow:

1. Fork the repository
2. Create a feature branch: git checkout -b feat/my-change
3. Commit changes: git commit -m "Add my feature"
4. Push and open a PR

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📄 License

Specify your license here (e.g., MIT). Add a LICENSE file to the repository.

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📫 Contact

For questions, feature requests, or help, open an issue or contact the repository owner: Ankitaghavate