student-depression-prediction-system

📌 Overview

This project predicts student depression using interpretable ML models and suggests interventions via counterfactuals.

Dataset: 27,900+ records (Kaggle link)
Model Used: Logistic Regression (No SMOTE)
F1 Score: 86.7%
Key Features: Academic pressure, suicidal thoughts, financial stress
Counterfactuals: Generated with DiCE to suggest minimal changes for positive outcomes

Name		Name	Last commit message	Last commit date
Latest commit History 4 Commits
DV Report Group 3.pdf		DV Report Group 3.pdf
DV_Project_Counterfactual_Insights_for_Student_Depression.twbx		DV_Project_Counterfactual_Insights_for_Student_Depression.twbx
Project_CF_generate (1).ipynb		Project_CF_generate (1).ipynb
Project_model_training (1).ipynb		Project_model_training (1).ipynb
README.md		README.md
student_depression_dataset.csv		student_depression_dataset.csv
student_depression_eda.ipynb		student_depression_eda.ipynb