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Agentic AI (LLM Layer)\n\n* **CrewAI** for multi-agent orchestration\n* **LangChain** for prompt and tool management\n* Agents collaborate to:\n\n  * Interpret user intent\n  * Plan itineraries\n  * Justify recommendations\n\n### 2. Machine Learning Layer\n\n| Task                     | Model                                  |\n| ------------------------ | -------------------------------------- |\n| Trip Type Classification | Decision Tree, Random Forest           |\n| POI Preference Scoring   | Naive Bayes                            |\n| Recommendation System    | Hybrid (Collaborative + Content-based) |\n| Itinerary Optimization   | Ranking + Constraint-based selection   |\n\n### 3. Explainability\n\n* **SHAP** used to explain:\n\n  * Why destinations were chosen\n  * Which user features influenced decisions\n* Example explanation:\n\n  > *“This destination was selected due to your preference for cultural activities, mid-range budget, and prior visit history.”*\n\n---\n\n## Datasets Used\n\n###  Points of Interest (POIs)\n\n* **Custom-built dataset**\n* Curated manually and programmatically\n* Contains:\n\n  * Location\n  * Category\n  * Estimated cost\n  * Popularity\n  * Activity type\n\n###  Users Dataset\n\n* **Kaggle-based + synthetic data**\n* User demographics, budgets, preferences\n\n###  User History\n\n* Generated using Python (fake but realistic values)\n* Past trips, interactions, and feedback\n* Enables collaborative filtering & analytics\n\n> ⚠️ No real personal data is used. All user data is anonymized or synthetic.\n\n---\n\n## 🗂️ Project Structure\n\n```text\nagentic_trip_planner/\n│── main.py                      # Entry point (agents + ML pipeline)\n│── trip_agents.py               # CrewAI agent definitions\n│── trip_tasks.py                # Agent task prompts\n│\n├── ml_models/\n│   ├── preprocess.py\n│   ├── destination_classifier.py\n│   ├── preference_scorer.py\n│   ├── recommender.py\n│   ├── optimizer.py\n│   └── explainability.py\n│\n├── data/\n│   ├── users.csv\n│   ├── user_history.csv\n│   └── pois.csv\n│\n├── ui/\n│   ├── app.py                   # Streamlit app\n│   └── components/\n│\n├── models/                      # Saved ML models (joblib)\n│\n├── requirements.txt\n└── README.md\n```\n\n---\n\n##  Installation & Setup\n\n### Create Virtual Environment\n\n```bash\npython -m venv venv\n```\n\nActivate:\n\n* **Windows**\n\n```bash\nvenv\\Scripts\\activate\n```\n\n* **Linux/Mac**\n\n```bash\nsource venv/bin/activate\n```\n\n---\n\n###  Install Dependencies\n\n```bash\npip install -r requirements.txt\n```\n\nCore libraries include:\n\n* `crewai`, `langchain`\n* `scikit-learn`, `pandas`, `numpy`\n* `shap`, `matplotlib`, `seaborn`\n* `streamlit`\n* `transformers`, `accelerate`\n\n---\n\n## ▶️ Running the Project\n\n### Run the Streamlit App\n\n```bash\nstreamlit run ui/app.py\n```\n\n### Run the Agentic Pipeline (CLI)\n\n```bash\npython main.py\n```\n\n---\n\n## Analytics & Dashboard\n\nThe Streamlit UI provides:\n\n* Popular destination trends\n* Budget distribution\n* User preference heatmaps\n* Trip history visualization\n* SHAP explanation plots\n\n---\n\n## Research & Novelty\n\n### Why this project is novel:\n\n* Combines **agentic LLM reasoning with classical ML**\n* Adds **explainability** to recommender systems\n* Uses **synthetic + real-world datasets** responsibly\n---\n\nFuture Enhancements\n\n* Deep learning–based preference modeling\n* Graph-based POI relationships\n* Reinforcement learning for itinerary optimization\n* Online learning from real user feedback\n* API-based deployment (FastAPI)\n","readmeExcerpt":"**An ML + LLM powered travel planning system using multi-agent reasoning, explainable ML, and interactive analytics** --- Overview This project implements a **personalized, explainable trip planning system** that combines: * **LLM-based agentic reasoning** (CrewAI + LangChain) * **Classical Machine Learning models** for recommendation and optimization * **Explainability tools (SHAP)** to justify decisions * **Interac","codeSnippets":[],"executableExamples":[{"language":"text","snippet":"User Input\n   ↓\nML Models (classification, scoring, recommendation)\n   ↓\nExplainability Layer (SHAP)\n   ↓\nCrewAI Agents (reasoning & planning)\n   ↓\nOptimized Itinerary\n   ↓\nStreamlit UI + Analytics Dashboard"},{"language":"text","snippet":"agentic_trip_planner/\n│── main.py                      # 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