Depression Detection from Survey Data

This project successfully developed a high-performance machine learning model to predict

depression from survey data, achieving 92.09% accuracy and securing the Top 8 ranking

on Kaggle among hundreds of competitors. The solution processes complex psychological,

educational, and lifestyle factors to identify at-risk individuals, providing actionable

insights for healthcare and organizational wellness programs.

Key Achievement: 92.09% accuracy in depression prediction with real-world applicability

for early intervention systems.