👉 Streamlit App: (add link)
Context-Aware AI Decision Automation is an explainable AI system simulating enterprise decision platforms that analyze customer requests, predict urgency, and recommend next best actions.
Unlike typical ML demos, this project focuses on:
- Decision automation
- Explainability
- ML + rule integration
- Enterprise data logging
- Production-style workflow simulation
| Feature | Description |
|---|---|
| 🧠 NLP Understanding | TF-IDF processing of unstructured requests |
| ⚡ ML Prediction | Interpretable urgency classification |
| 📋 Rule Engine | Business rules for decision logic |
| 🔍 Explainability | Reasoning behind each action |
| 🗂️ Logging | SQLite + CSV audit logs |
| 🏢 Enterprise Simulation | Customer support workflow mimic |
User Request + Context
↓
Text Preprocessing (NLP)
↓
Machine Learning Model
↓
Urgency Prediction
↓
Rule-Based Decision Engine
↓
Recommended Action + Logs
👉 Add demo GIF here
👉 Add UI screenshot here
Request: “My payment failed and no one is responding”
Customer Type: Premium
Interaction Count: 3
Severity Score: 8
🚨 Urgency: HIGH
✅ Action: Escalate to Human Support
| Layer | Technology |
|---|---|
| Language | Python |
| UI | Streamlit |
| ML | Scikit-learn (Logistic Regression) |
| NLP | TF-IDF |
| Rules | Custom business logic |
| Storage | SQLite + CSV |
- Accuracy: XX%
- Precision: XX%
- Recall: XX%
- Decision latency: XX ms
- Logging throughput: XX req/sec
- Training data separated from live logs
- Continuous decision logging
- Improved explainability
- Hardened CSV loading
- Enhanced UI
- Periodic retraining pipeline
- Confidence scoring
- Analytics dashboard
- REST API
- LLM reasoning layer
git clone https://github.com/YOURUSERNAME/REPO
cd REPO
pip install -r requirements.txt
streamlit run app.py├── data
├── models
├── logs
├── app.py
├── rules_engine.py
├── preprocessing.py
├── requirements.txt
Naveen Kumar
📧 kotnananaveenkumar620@gmail.com
💼 https://www.linkedin.com/in/naveen-kumar-kotnana-a592571b3/
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