Depression & Mental Health Classification
Description
This dataset is derived from a mental health and depression survey, containing 1,998 cleaned responses with demographic, lifestyle, behavioral, and psychological features. The primary objective of the dataset is to support mental health classification tasks, particularly in predicting different types of depression such as job-related, family-related, or love-related depression. The dataset includes information on age, gender, education, employment status, symptoms, lifestyle habits (sleep, eating, social media usage), coping strategies, and mental health support availability. Missing values, duplicates, and irrelevant text fields have been carefully preprocessed to ensure high quality and usability. Researchers, students, and practitioners can use this dataset for: Multi-class classification tasks (predicting depression types) Exploratory data analysis on mental health patterns Feature importance and explainability studies (e.g., LIME/SHAP) Developing early detection models for mental health support By contributing this dataset, the goal is to encourage data-driven approaches to mental health awareness, prevention, and support systems.