AIML
🧠 Smart HR Analytics Dashboard
AI-powered workforce intelligence platform for employee attrition prediction, sentiment analysis, and natural language HR queries.
🚀 Live Demo
https://rajkumar123887.github.io/AIML/
✨ Features
| Module | Description |
|---|---|
| 📊 Attrition Overview | Risk charts, salary scatter plots, tenure heatmaps, feature importances |
| 👥 Employee Risk Table | Color-coded risk register with CSV export |
| 💬 Sentiment Analysis | NLP morale scoring per department with HR alerts |
| 🤖 AI Query Interface | Ask HR questions in plain English (GPT + rule-based fallback) |
🛠 Tech Stack
- Frontend: Streamlit, Plotly
- ML Model: Random Forest (scikit-learn)
- NLP: TextBlob sentiment analysis
- AI: LangChain + OpenAI GPT (optional)
- Data: Faker (synthetic HR data generation)
⚡ Quick Start
# 1. Clone the repo
git clone https://github.com/rajkumar123887/AI-ML-PAYROLL.git
cd AI-ML-PAYROLL
# 2. Install dependencies
pip install -r requirements.txt
# 3. Download TextBlob data
python -m textblob.download_corpora
# 4. Run the app
streamlit run app.py
App opens at http://localhost:8501
📁 Project Structure
AI-ML-PAYROLL/
├── app.py # Main Streamlit dashboard
├── requirements.txt # Python dependencies
├── utils/
│ ├── __init__.py
│ ├── ai_interface.py # AI query engine (GPT + rule-based)
│ ├── data_generator.py # Synthetic HR data generator
│ ├── model.py # Random Forest training & prediction
│ ├── preprocessing.py # Feature engineering pipeline
│ └── sentiment.py # NLP sentiment analysis
├── data/ # Generated data (runtime)
├── models/ # Saved models (runtime)
└── assets/ # Static assets
☁️ Deploy on Streamlit Cloud (Free)
- Push this repo to GitHub
- Go to share.streamlit.io
- Click New app → Select this repo
- Set Main file path to
app.py - Click Deploy 🎉
📸 Screenshots
Dashboard includes dark-themed interactive charts, employee risk tables, and an AI chat interface.
🤝 Contributing
Pull requests are welcome! For major changes, please open an issue first.
Built with ❤️ by rajkumar123887