Mental Health Prediction Model

This project uses Machine Learning (ML) to predict mental health conditions in individuals based on their demographic and social information. The goal is to build a model that can be useful for early screening and intervention.

Technology Stack

This project uses Python for the core ML logic, but the concepts are designed to be easily integrated into a full-stack web application (e.g., using Node.js/React as a deployment layer).

• Core ML Language: Python
• Libraries: Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
• Models Evaluated: AdaBoost, Random Forest, Decision Tree, Logistic Regression, and more.

Methodology: How We Built It

We followed a standard ML workflow, focusing on high accuracy and clear results:

1. Data Preparation: Cleaned the raw dataset (from Kaggle) by handling missing values and converting text data into a format suitable for the models.
2. Feature Selection (Wrapper Approach): To ensure the model focused only on the most predictive factors, we used a wrapper approach. This means we used the model itself as a tool to test different combinations of features, finding the optimal subset for the best performance.
3. Model Selection: We tested various classification algorithms to find the best fit. AdaBoost Classifier was ultimately selected as the top performer.
4. Model Evaluation: Measured the model's performance using robust metrics.

Key Results

• Best Performing Model: AdaBoost Classifier.
• Overall Accuracy: The final model achieved an accuracy of 86.93% in predicting mental health conditions.

Advanced Evaluation & Future Features

We implemented advanced features to give a clearer, more detailed view of the model's performance beyond simple accuracy. We plan to integrate these plots into a web dashboard later:

• ROC Curve (Receiver Operating Characteristic): Shows the model's ability to distinguish between individuals with and without a predicted condition.
• Precision-Recall Curve: Provides detailed insight into the trade-off between the model's correct positive predictions and its ability to find all relevant cases.
• Feature Importance Plot: Clearly visualizes which demographic or social factors are most heavily weighted by the model when making a prediction.

How to Run Locally

1. Clone the project:
   git clone [repository-link-here]

2. Install requirements:
   pip install -r requirements.txt

3. Run the script:
   python main.py

   Follow the on-screen console instructions for input and results.

Conclusion

This model is a strong foundation for predicting mental health conditions. Future steps involve turning this core Python logic into a Full Stack web application for wider accessibility and practical use.