Predict your chances of getting shortlisted — and receive personalized feedback to enhance your resume.
This project simulates how companies might use data to evaluate resumes. It allows users to input resume-related details like skills, education background, experience type, and target company, then predicts shortlisting probability using a trained machine learning model.
Alongside the prediction, the app also provides actionable suggestions based on feature importance — helping users understand what could make their profile stronger.
⚡ Built as a hands-on learning project to explore real-world machine learning workflows with simple UI deployment.
- ✅ Predicts resume shortlisting probability
- ✅ Company-specific targeting and location awareness
- ✅ Personalized resume improvement feedback using feature importance
- ✅ Clean and intuitive web interface (Streamlit)
- ✅ Modular backend using pickled ML models and encoded data
- Categorical, one-hot, and multi-hot encoding
- Feature engineering for resume data
- Random Forest classification with evaluation metrics
- Feature importance analysis for feedback generation
- Serialization with Pickle
- UI deployment using Streamlit
- Demonstrates how ML can assist in resume screening
- Bridges the gap between data science and deployment
- Emphasizes explainability — not just prediction
- Designed to explore a full ML pipeline in a practical context
- Resume upload (PDF parsing and auto-fill)
- Advanced model tuning (GridSearchCV, XGBoost, etc.)
- Explainability with SHAP or LIME
- Deployment to Streamlit Cloud or Hugging Face Spaces
This project is built for learning purposes, not to replicate or endorse automated hiring decisions. Real-world recruitment involves far more complexity, fairness considerations, and human judgment.