Overview:
This project explores societal and environmental factors contributing to mental health disorders, going beyond economic conditions to uncover key influences like green spaces, CO₂ emissions, and research investments. Using data analytics and machine learning, the study aims to identify patterns, predict anxiety prevalence, and suggest data-driven interventions for improving mental well-being.
Key Objectives:
✅ Identify Global Mental Health Trends – Analyze datasets to uncover patterns in mental health disorders across countries.
✅ Understand Environmental & Economic Influences – Examine the impact of GDP, education, healthcare spending, CO₂ emissions, and renewable energy on mental health.
✅ Predict Anxiety Prevalence – Develop machine learning models (Random Forest, Clustering, 3D Linear Regression) to forecast anxiety rates.
✅ Propose Policy Recommendations – Advocate for nature-integrated therapies, sustainability policies, and increased research investment.
Data & Methodology:
📊 Datasets Used:
- Global Trends in Mental Health Disorders (Our World in Data)
- World Development Indicators (World Bank)
- Welcome Global Monitor (Mental health perceptions survey)
🛠 Tools & Techniques:
- Data Wrangling & Analysis: Python (Pandas, NumPy), Excel
- Visualization & Exploration: Tableau, Power BI
- Machine Learning: Random Forest (98% R²), K-Means Clustering, 3D Linear Regression
Key Insights & Findings:
📌 Life expectancy is the most significant predictor of anxiety prevalence.
📌 Green spaces reduce mental health risks, reinforcing nature’s role in mental well-being.
📌 Higher CO₂ emissions and reduced research spending correlate with increased anxiety disorders.
📌 Investment in research & sustainable policies can improve mental health outcomes.
Impact & Future Scope:
This project provides evidence-based insights for policymakers, healthcare professionals, and environmental advocates to integrate sustainability-focused mental health interventions. Future work can explore real-time mental health tracking, social determinants, and policy-based implementations to enhance global mental well-being.