This synthetic mental health dataset has been generated as an educational resource for data science, machine learning, and data analysis applications in mental healthcare. The data focuses on key treatment metrics, such as symptom severity, therapy types, and emotional states, which are important for understanding and improving mental health care outcomes. This dataset is designed to help users practice data manipulation, analysis, and predictive modeling in a mental health context.

Dataset Features:

• Patient_Id: Unique identifier for each patient.
• Age: Age of the patient (in years).
• Gender: Gender of the patient (e.g., "female," "male").
• Diagnosis: Diagnosed mental health condition (e.g., "Anxiety," "Depression," "Bipolar Disorder").
• Symptom_Severity: (1-10) Severity of symptoms, measured on a scale of 1 (mild) to 10 (severe).
• Mood_Score: (1-10) Self-reported mood score during treatment.
• Sleep_Quality: (1-10) Quality of sleep as reported by the patient, on a scale of 1 (poor) to 10 (excellent).
• Physical_Activity: Number of hours of physical activity per week.
• Medication: Medications prescribed to the patient (e.g., "SSRIs," "Antidepressants").
• Therapy_Type: Type of therapy provided (e.g., "CBT," "DBT"). Start_Date: Date when the treatment began. Duration_Weeks: Duration of treatment in weeks.
• Stress_Level: (1-10) Stress level as reported by the patient, on a scale of 1 (low) to 10 (high).
• Outcome: Treatment outcome (e.g., "Improved," "Deteriorated").
• Progress: (1-10) Progress made during treatment, rated on a scale of 1 to 10.
• Emotional_State: Emotional state detected during the treatment process (e.g., "Happy," "Anxious").
• Adherence (%): Percentage of adherence to the prescribed treatment plan.

Distribution:

Usage:

This dataset is useful for a variety of applications, including:

Mental Health Research: To explore relationships between treatment types, patient adherence, and treatment outcomes. Educational Training: To practice data cleaning, transformation, and visualization techniques specific to mental health data. Predictive Modeling: To develop models that predict treatment outcomes or adherence based on patient demographics and therapy details.

Correlation Heatmap of Numerical Variables:

Coverage:

This dataset is synthetic and anonymized, making it a safe tool for experimentation and learning without compromising real patient privacy.

License:

CCO (Public Domain)

Who can use it:

Researchers and educators: For academic studies or teaching purposes in mental healthcare analytics and data science. Data science enthusiasts: For learning, practising, and applying mental healthcare data manipulation and analysis techniques. Healthcare professionals: For analyzing and predicting mental health treatment outcomes and exploring trends in therapy effectiveness.