Mental Health Prediction System

This project predicts mental health conditions using a machine learning model trained with the Random Forest algorithm. The model is deployed using a Flask API, while a Spring Boot API handles user interactions, stores data, and sends predictions via email.

Tech Stack

• Machine Learning Model: Random Forest
• Backend APIs: Flask (ML Model), Spring Boot (User Interaction & Email)
• Database: (Specify if used)
• Deployment: Local or Cloud

How to Run the Project

1. Prepare the Dataset

• Create or load the dataset required for training.

2. Train the Model

• Train the model using the Random Forest algorithm.
• Save the trained model for later use.

3. Start the Flask API

• Run the Flask application to deploy the ML model.
• Ensure the API is accessible for predictions.

4. Start the Spring Boot Application

• Run the Spring Boot API to handle user requests.

5. Make a Prediction Request

• Send a request via the Spring Boot API, which forwards it to the Flask API.
• The Flask API processes the data and returns the prediction.
• The result is sent to the user via email.

Endpoints

• Flask API – Handles ML model inference.
• Spring Boot API – Manages user requests, stores data, and emails results.

License

This project is open-source. Feel free to modify and enhance it.