🧠 AI-Powered Event Service Recommendation Model

A machine learning-based recommendation engine for event organizers to get the best venue, caterer, and decorator suggestions based on user reviews, ratings, and location.

Built as a core module of the NexEvent project.

---

🎯 Objective

Help users efficiently discover and choose the top-rated event service providers in their locality using AI.
Key goals:

• Rank services (Venue, Caterers, Decorators) based on rating and popularity
• Incorporate user preferences and location filtering
• Deliver top 3–5 smart recommendations using ML

---

⚙️ Features

✅ Predicts top-rated services based on:

• Average Rating
• Number of Reviews
• Service Type (venue, caterer, etc.)
• Location (city-based filter)

✅ Real-time recommendation API
✅ Integrated with NexEvent frontend
✅ Clean, preprocessed datasets for training

---

🧪 Tech Stack

Component	Tech Used
Programming Lang	Python
ML Framework	TensorFlow, scikit-learn
Backend API	Flask
Data Handling	Pandas, NumPy
Deployment	flask server
Frontend	Reactjs,Typescript,Vue

---

🧪 Running Locally

1. Clone the repo

git clone https://github.com/tejashgawas/event-recommendation-model.git cd event-recommendation-model

2. Install requirements

pip install -r requirements.txt

3. Run the Flask server

python app.py

4. Hit API

http://localhost:5000/recommend?type=venue&city=Goa

🏗 Architecture Overview

📦 Dataset (CSV)
   └── service_name, rating, user_rating_count, service_type, city

⬇️

🔄 Preprocessing:
   - Normalize ratings
   - Encode service types
   - One-hot city encoding

⬇️

🧠 TensorFlow Deep Neural Network Model combining popularity and geolocation based filtering
   - Input: [rating, review_count, city, type]
   - Hidden layers: Dense (ReLU)
   - Output: Predicted score for the recommendation

⬇️

📡 Flask API Endpoint:
   /recommend?event_type=Wedding&city=Goa

⬇️

📱 NexEvent Frontend UI:
   - Fetches recommendations
   - Displays top results
---