Student Depression Dataset.

A student depression dataset typically contains data aimed at analyzing, understanding, and predicting depression levels among students. It may include features such as demographic information (age, gender), academic performance (grades, attendance), lifestyle habits (sleep patterns, exercise, social activities), mental health history, and responses to standardized depression scales.

These datasets are valuable for research in psychology, data science, and education to identify factors contributing to student depression and to design early intervention strategies. Ethical considerations like privacy, informed consent, and anonymization of data are crucial in working with such sensitive information.

1. File Structure

• Format: CSV format
• Rows: Each row represents an individual student.
• Columns: Each column represents a specific feature or attribute.

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2. Columns

• ID: Unique identifier for each student.
• Age: Age of the student.
• Gender: Gender (e.g., Male, Female).
• City: Geographic region
• CGPA: Grade Point Average or other academic scores.
• Sleep Duration: Average daily sleep duration.
• Profession:
• Work Pressure:
• Academic Pressure:
• Study Satisfaction:
• Job Satisfaction:
• Dietary Habits:
  And much more

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3. Target Variable

• Depression_Status: Binary (Yes/No)