🚀 Tech Trend Radar

A comprehensive platform for monitoring and predicting emerging technology trends using AI and multiple data sources.

🎯 Features

• Real-time Technology Monitoring: Track emerging technologies across GitHub, patents, research papers, and job postings
• AI-Powered Trend Prediction: Machine learning models predict which technologies will gain traction
• Interactive Dashboard: Beautiful web interface for exploring trends and insights
• Trend Scoring: Proprietary algorithm scores technologies based on adoption potential
• Market Intelligence: Actionable insights for businesses and innovators
• API Access: RESTful API for programmatic access to trend data

🏗️ Architecture

tech-trend-radar/
├── backend/                 # API server and ML models
│   ├── api/                # REST API endpoints
│   ├── models/             # Machine learning models
│   ├── data_collectors/    # Data collection modules
│   └── processors/         # Data processing pipelines
├── frontend/               # React web dashboard
│   ├── components/         # UI components
│   ├── pages/             # Dashboard pages
│   └── utils/             # Helper functions
├── data/                   # Raw and processed data
├── notebooks/              # Jupyter notebooks for analysis
└── scripts/               # Utility scripts

🛠️ Technology Stack

• Backend: Python, FastAPI, PostgreSQL, Redis
• Frontend: React, TypeScript, Chart.js, Tailwind CSS
• ML/AI: scikit-learn, TensorFlow, pandas, numpy
• Data Sources: GitHub API, Google Patents, arXiv, job boards
• Deployment: Docker, GitHub Actions

🚀 Getting Started

Prerequisites

• Python 3.9+
• Node.js 16+
• PostgreSQL
• Redis

Installation

1. Clone the repository

git clone https://github.com/yourusername/tech-trend-radar.git
cd tech-trend-radar

2. Install backend dependencies

cd backend
pip install -r requirements.txt

3. Install frontend dependencies

cd frontend
npm install

4. Set up environment variables

cp .env.example .env
# Edit .env with your API keys and database config

5. Initialize the database

cd backend
python scripts/init_db.py

6. Run the application

# Backend
cd backend
uvicorn main:app --reload

# Frontend
cd frontend
npm start

📊 Data Sources

• GitHub: Repository trends, star growth, language adoption
• Patents: Patent filings, citations, technological domains
• Research Papers: arXiv, Google Scholar, academic trends
• Job Market: Technology skill demands, salary trends
• Social Media: Twitter mentions, LinkedIn discussions

🤖 Machine Learning Models

• Trend Detection: Time series analysis for identifying emerging patterns
• Adoption Prediction: Forecasting technology adoption curves
• Sentiment Analysis: Understanding community perception
• Clustering: Grouping related technologies and trends

🔧 API Endpoints

• GET /api/trends - Get current technology trends
• GET /api/predictions - Get trend predictions
• GET /api/technologies/{tech_id} - Get specific technology details
• POST /api/analyze - Analyze custom technology queries

📈 Usage Examples

import requests

# Get current trends
response = requests.get('http://localhost:8000/api/trends')
trends = response.json()

# Get predictions for a specific technology
response = requests.get('http://localhost:8000/api/predictions?tech=quantum-computing')
predictions = response.json()

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Thanks to the open-source community for providing excellent tools and datasets
• Special recognition to Kaggle for hosting valuable datasets
• Inspired by the need for better technology trend intelligence

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