CLIENT / STUDENT PROJECT

Adolescent Depression Prediction System

Machine Learning · Predictive Analytics

Client · Student project Available

Academic machine-learning system exploring patterns in adolescent data associated with depression-related risk indicators.

Pythonscikit-learnXGBoostSMOTETomek

The Adolescent Depression Prediction System is an academic machine-learning project designed to study relationships between supplied adolescent data and depression-related risk indicators.

The system is intended as a predictive analytics and research exercise, not as a clinical diagnostic product.

CHALLENGE

Real-world health-related datasets can contain imbalanced classes and complex relationships between input variables.

A model may become biased toward the majority class if the data distribution is not handled carefully, while results must also be presented responsibly because the subject involves mental health.

SOLUTION

The project uses Python and scikit-learn-based machine-learning workflows, with XGBoost as part of the modelling work.

SMOTETomek is included in the data-processing workflow to address class imbalance by combining oversampling and data-cleaning techniques.

OUTCOME

The project demonstrates an end-to-end academic predictive analytics workflow from data preparation through model development and prediction.

It should be described as academic decision support or predictive research only. It is not a medical diagnosis and should not be presented as a replacement for qualified mental-health professionals.