🧠 AI Resume + Job Matcher with Feedback Loop

A smart, self-learning system that matches resumes to job descriptions using NLP and machine learning. Built to simulate how companies like Google or LinkedIn screen candidates at scale.

🚀 Features

• Cleans and vectorizes resume text using TF-IDF
• Classifies resumes into job categories with 98%+ accuracy using SVM
• Includes a recruiter feedback loop: learns from incorrect predictions
• Logs feedback to CSV for future retraining
• Modular, CLI/Colab-ready, no frontend/backend

📁 Files

• resume_matcher.ipynb – full working notebook (Colab-ready)
• feedback_log.csv – records corrected labels from user feedback
• sample_data/resumes.csv – sample resume texts
• sample_data/job_descriptions.csv – job listings to match against

🛠 Tech Used

• Python, Scikit-learn, NLTK, Pandas
• TF-IDF for vectorization
• Support Vector Machine (SVM) classifier

📈 Results

• Accuracy: 98.4% on test data
• F1 Score: 0.98 average
• Handles 10+ job categories

💡 Unique Selling Point

Unlike basic resume screeners, this project improves itself via a feedback loop, learning from recruiter corrections — just like smart hiring systems in real companies.

✅ How to Run

1. Open resume_matcher.ipynb in Google Colab
2. Upload resumes.csv and job_descriptions.csv
3. Run cells to train, predict, and log feedback
4. Download feedback_log.csv for model updates