Student Depression Dataset

Key Features of the Dataset

1. Data Overview

• File Format: Provided in CSV format for easy integration into analysis tools.
• Structure:
  • Rows: Each row represents data from an individual student.
  • Columns: Attributes include demographic, academic, lifestyle, and psychological factors.

2. Columns and Attributes

• ID: A unique identifier for each student.
• Age: Student’s age for demographic analysis.
• Gender: Gender information (e.g., Male, Female, or Non-binary).
• City: Geographic location for understanding regional influences.
• CGPA: Academic performance metrics such as Grade Point Average.
• Sleep Duration: Average daily sleep hours for lifestyle assessment.
• Profession: Student’s part-time or full-time profession, if any.
• Work Pressure: Levels of work-related stress.
• Academic Pressure: Stress due to academic workload.
• Study Satisfaction: Self-reported satisfaction with study habits and outcomes.
• Job Satisfaction: For working students, satisfaction with their jobs.
• Dietary Habits: Eating patterns that may influence mental health.
• Depression_Status: Target variable indicating depression levels (Yes/No).

Advantages of the Student Depression Dataset

1. Mental Health Research
   Enables psychologists and researchers to identify critical factors contributing to student depression, such as academic pressure, work-life balance, and lifestyle habits.
2. Educational Insights
   Assists educators in understanding how academic stress and study satisfaction affect mental health, fostering improvements in learning environments.
3. Policy Making
   Provides data-driven evidence to support mental health policies and resource allocation in schools, colleges, and universities.
4. Machine Learning Applications
   Ideal for training predictive models to detect early signs of depression, allowing timely interventions and preventive measures.
5. Holistic Analysis
   Combines demographic, academic, and psychological data to give a comprehensive view of the factors affecting mental health.
6. Customizable and Scalable
   The dataset structure supports feature addition for advanced studies, making it suitable for diverse research and analysis requirements.

Ethical Considerations

Given the sensitive nature of the data, ethical guidelines such as privacy protection, informed consent, and anonymization have been prioritized. Researchers are encouraged to uphold these principles during data handling and analysis.

Applications

• Build predictive models for mental health risk assessment.
• Analyze the impact of lifestyle and academic factors on depression.
• Develop targeted mental health interventions for students.
• Study correlations between academic performance and mental health.

Download the Dataset

Start your research journey with the Student Depression Dataset today. Gain insights to promote mental well-being among students and contribute to meaningful solutions in mental health and education.