Global Livability Analysis Dashboard

Overview

This project is a data-driven dashboard that ranks countries based on livability metrics. It combines public sentiment analysis with economic and environmental indicators to provide policymakers and investors with actionable insights.

🔹 Sentiment Analysis: Extracts public opinions from Reddit and categorizes sentiment for different aspects (Healthcare, Economy, Environment, etc.). 🔹 World Bank Data Analysis: Uses historical data and Facebook Prophet to forecast livability scores for each country.

Built using Python, Streamlit, Plotly, and Machine Learning.

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Features

1. Sentiment Analysis

• Real-time Reddit data extraction
• NLP-based sentiment classification (Strongly Negative → Strongly Positive)
• Categorization of discussions into Healthcare, Economy, Environment, etc.
• Geographical mapping of sentiment trends across countries
• Interactive filters for policymakers to explore specific concerns

2. Livability Score Forecasting (World Bank Data + Facebook Prophet)

• Data gathered from the World Bank API
• Facebook Prophet time-series forecasting (Predicts future livability rankings till 2027)
• Comparison of historical vs. forecasted scores
• Key indicator analysis: GDP per capita, Life Expectancy, Literacy Rate, CO2 Emissions, etc.
• Factor correlation analysis: Identifies which indicators influence livability the most

3. Dashboard Functionalities

• Interactive visualizations (Plotly charts, geo-maps, bar charts)
• Policy recommendations based on sentiment trends
• Country-wise comparison & ranking system
• Modern UI with custom CSS for an enhanced experience

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Tech Stack

• Frontend: Streamlit, Plotly, HTML/CSS
• Backend: Python (Flask for API handling)
• Data Processing: Pandas, NumPy
• Machine Learning: Facebook Prophet (for forecasting), NLP (Sentiment Analysis)

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Installation & Setup

1 Clone the Repository

git clone https://github.com/your-username/livability-dashboard.git
cd livability-dashboard

2️ Install Dependencies

pip install -r requirements.txt

3️ Run the Dashboard

streamlit run dashboard.py

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Future Enhancements

Integrate real-time sentiment monitoring Improve forecasting models with advanced ML techniques Add more country-specific factors for deeper analysis

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Contributing

Feel free to open an issue or submit a pull request to enhance the dashboard!

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License

This project is licensed under the MIT License.