Mental-Health-Score-Predictor

Mental Health Score Predictor

A regression model that predicts a person's Mental Health Score (0–10) from their social media usage habits, sleep, study, physical activity, and stress level.

The goal is simple: understand how daily digital habits and lifestyle factors relate to mental well-being, and offer a working baseline model others can build on.

Model Details

• Model type: Random Forest Regressor (scikit-learn)
• Task: Tabular regression
• Target variable: Mental_Health_Score (continuous, 0–10)
• Framework: scikit-learn, trained inside a Pipeline
• Saved format: .pkl (via joblib)

Intended Use

• Educational / portfolio project exploring the link between digital habits and mental well-being.
• Baseline for further experimentation (feature engineering, other algorithms, hyperparameter tuning).

Not intended for: clinical, diagnostic, or any real-world mental health decision-making. This model is trained on a small, self-reported survey-style dataset and does not represent a validated psychological assessment.

Dataset

• Size: 5,000 rows, 13 columns
• Features used:
  • Age, Gender, Country, Academic_Level
  • Most_Used_Platform, Purpose_Of_Use
  • Avg_Daily_Usage_Hours, Daily_Unlocks
  • Study_Hours, Physical_Activity_Hours, Sleep_Hours_Per_Night
  • Stress_Level (ordinal: Low → Very High)
• Target: Mental_Health_Score
• No missing values in the raw data.

Preprocessing

• Duplicates removed; Physical_Activity_Hours clipped at 0 (no negative values).
• Country grouped into top-10 countries + "Other" to reduce cardinality.
• Study_Hours (right-skewed) log-transformed, then scaled.
• Other numeric features standard-scaled.
• Stress_Level ordinal-encoded (Low → Very High).
• Remaining categorical features one-hot encoded.
• 80/20 train-test split.

Training & Evaluation

Four models were compared, with Random Forest tuned via Grid Search and Randomized Search:

The default Random Forest performed best on the test set and is the model saved (Mental_Health_Model.pkl). Tuned versions traded a bit of test performance for lower variance — worth exploring further if generalization to new data is a priority.

How to Use

import joblib
import pandas as pd

model = joblib.load("Mental_Health_Model.pkl")

sample = pd.DataFrame([{
    "Age": 21,
    "Gender": "Male",
    "Academic_Level": "Undergraduate",
    "Most_Used_Platform": "Instagram",
    "Purpose_Of_Use": "Entertainment",
    "Avg_Daily_Usage_Hours": 4.5,
    "Daily_Unlocks": 140,
    "Study_Hours": 4.0,
    "Physical_Activity_Hours": 2.0,
    "Sleep_Hours_Per_Night": 6.5,
    "Stress_Level": "Medium",
    "Grouped_Country": "Other"
}])

prediction = model.predict(sample)
print(f"Predicted Mental Health Score: {prediction[0]:.2f}")

Note: the model expects the same preprocessing pipeline it was trained with (it's bundled inside the saved Pipeline), so raw feature columns can be passed directly as shown above.

Limitations

• Trained on a single, likely synthetic/survey-style dataset — may not generalize to real-world populations.
• Self-reported features (usage hours, stress level) are prone to bias.
• Correlation-based insights should not be read as causation.
• No fairness/bias audit across demographic groups has been performed.

Files

• notebook.ipynb — full workflow: EDA, cleaning, feature engineering, training, tuning, and evaluation.
• data.csv — training dataset.
• Mental_Health_Model.pkl — trained Random Forest pipeline.

License

Released under the MIT License. Use freely for learning and experimentation.