A Data-Driven Approach to Understanding Adolescent Suicide Trends 🚀
🏆 Big Data Competition 2024 Finalist 🏆
This project leverages open data analytics and machine learning to analyze risk factors, trends, and disparities in adolescent suicide rates. The dashboard provides interactive visualizations to support mental health awareness, policy-making, and prevention strategies.
🔹 Finalist in the Big Data Competition 2024, under the theme "Harnessing AI & Open Data to Combat Social Inequities Among Adolescents."
🔹 Uses machine learning and statistical modeling to identify key risk factors.
🔹 Provides interactive dashboards for data visualization, geographical insights, and demographic analysis.
🔹 Aims to support mental health professionals, policymakers, and researchers in understanding adolescent mental health trends.
✅ Data-Driven Insights: Analyzes risk factors including socioeconomic status, bullying, social media use, and mental health disorders.
✅ Interactive Dashboard: Allows users to explore trends by region, age group, and contributing factors.
✅ Machine Learning Predictions: Implements predictive modeling to identify high-risk populations.
✅ Open Data Integration: Uses datasets from WHO, CDC, national health surveys, and social platforms.
✅ User-Friendly Interface: Designed for researchers, policymakers, and mental health professionals.
