Depression & Mental Health Classification

Is derived from a structured mental health and depression survey and contains 1,998 cleaned responses. It includes 21 demographic, lifestyle, behavioral, and psychological features, with the primary objective of supporting multi-class depression classification tasks.

Each record is labeled with one of twelve clinically motivated depression types, making the dataset particularly suitable for supervised learning, explainable AI studies, and mental health analytics.

🎯 Target Variable: Depression_Type

The target variable is numerically encoded as follows:

Code Depression Type (Academic Naming)
0 No clinically significant depression
1 Minimal / Mild depression
2 Moderate depression
3 Moderately-severe depression
4 Severe depression
5 Persistent depressive disorder (Dysthymia)
6 Seasonal affective pattern
7 Peripartum / Postpartum depression
8 Bipolar-related depressive episode
9 Situational / Reactive depression
10 Psychotic depression
11 Other specified depressive disorder

These class names are aligned with commonly recognized categories in clinical and academic research and are suitable for use directly in scholarly publications.

🧩 Feature Variables and Encoding Scheme

All categorical variables were numerically encoded to support statistical analysis and machine learning models. The encodings follow logical ordinal or nominal mappings, as outlined below:

Gender:
0 = Male, 1 = Female

Education_Level:
0 = Primary or below
1 = Secondary / High school
2 = Undergraduate
3 = Postgraduate or higher

Employment_Status:
0 = Unemployed
1 = Student
2 = Employed
3 = Self-employed
4 = Other

Symptoms:
Encoded numerically to represent different symptom clusters (e.g., sleep disturbance, appetite loss, etc.)

Low_Energy:
0 = No, 1 = Yes, 2 = Occasionally

Low_SelfEsteem:
0 = No, 1 = Yes, 2 = Occasionally

Search_Depression_Online:
0 = No, 1 = Yes

Worsening_Depression:
0 = No, 1 = Yes

Overeating Level:
0 = None
1–4 = Mild
5–8 = Moderate
9–12 = Severe
(Grouped for interpretability)

Eating Frequency (per day):
0 = ≤2 meals
1 = 3 meals
2 = 4–5 meals
3 = >5 meals

SocialMedia_WhileEating:
0 = Never
1 = Rarely
2 = Often
3 = Always

Self_Harm:
0 = No history
1 = History of self-harm

Mental_Health_Support:
0 = No
1 = Yes

Suicide_Attempts:
0 = None
1 = Once
2 = Twice
3 = Three or more

All variables were checked, and no missing values remain after preprocessing.

⚙️ Data Preprocessing & Normalization

Removed non-informative identifiers and irrelevant text fields

Verified zero missing values across all 21 features

Standardized continuous variables (e.g., Depression_Score, Sleep_Hours) using Z-score normalization

Split data into 80% training and 20% testing

Feature Selection

Using the ANOVA F-test, the following top features were identified as most discriminative:

Education_Level

Employment_Status

Symptoms

Low_Energy

Search_Depression_Online

Worsening_Depression

Eating Frequency

SocialMedia_WhileEating

Nervous_Level

Mental_Health_Support