Traveler Backend Repository (Traveler)

Traveler – Backend Server

This repository contains the backend server for the Traveler platform, a social platform for travel enthusiasts. It handles user authentication, travel story storage, route management, gamification logic, notifications, and social feed functionality.

Frontend repository: Traveler Frontend ML Model repository: Travel-Advisor

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Visit Online : Traveler

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System Overview

• Backend: Node.js + Express.js
• Database: MongoDB
• Authentication: JWT-based secure login/signup
• API: RESTful endpoints consumed by frontend
• Notifications & Social Feed: Real-time updates via socket.io
• Integration: Calls the ML engine in Travel-Advisor for AI-powered destination recommendations

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Requirements

• Node.js (LTS recommended)
• npm or yarn
• MongoDB (local or cloud)
• Running ML engine (Travel-Advisor)

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Installation

1. Clone the repository

git clone https://github.com/hammadmeer-dev/Traveler.git
cd Traveler

2. Install dependencies

npm install
# or
yarn install

3. Configure environment variables (.env)

PORT=5000
MONGO_URI=your_mongo_connection_string
JWT_SECRET=your_jwt_secret
ML_API_URL=http://localhost:5001

4. Start the backend

npm run dev
# or
yarn dev

The API runs on http://localhost:5000 by default.

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Features

• User authentication (signup/login)
• Travel stories CRUD
• Gamification (badges, ranks)
• Social feed (like, comment, follow)
• Notifications (real-time)
• AI-powered recommendations via ML engine

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Contribution

• Fork → Branch → Commit → Push → Pull Request

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License

MIT License

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Contact

Hammad Farooq Meer – GitHub

Travel-Advisor Repository (ML)

Travel-Advisor – AI/ML Engine for Traveler Platform

This repository contains the machine learning engine for the Traveler platform. It provides AI-powered travel recommendations used by the backend server (Traveler).

Frontend repository: Traveler Frontend Backend repository: Traveler Backend

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Overview

• ML Algorithm: K-Nearest Neighbors (KNN) with Cosine Similarity
• Inputs: district (e.g., Punjab, KPK, Sindh), category (e.g., Fort, Valley, Museum)
• Outputs: Top 5 recommended destinations
• Tools: Python, Flask, Scikit-learn, Pandas, NumPy, SentenceTransformer
• Data: Tourist Destinations.csv

This engine is minimal in functionality but can be expanded. Contributions are welcome.

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Requirements

• Python 3.x
• Flask, Scikit-learn, Pandas, NumPy, SentenceTransformer

Install dependencies:

pip install -r requirements.txt

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Running the ML Engine

1. Clone the repository

git clone https://github.com/hammadmeer-dev/Travel-Advisor.git
cd Travel-Advisor

2. Start Flask server

python app.py

3. Connect with Backend Ensure the backend (Traveler) points to the ML API (ML_API_URL=http://localhost:5001) to fetch AI recommendations.

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Contribution

• Fork → Branch → Commit → Push → Pull Request

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License

MIT License

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Contact

Hammad Farooq Meer – GitHub