https://github.com/wizardoftrap/mental-health-predicter
The Mental Health Prediction System utilizes a Flask API to deploy the machine learning model, while a Spring Boot API handles user interactions, stores data, and sends personalized mental health predictions via email.
https://github.com/wizardoftrap/mental-health-predicter
Last synced: about 1 month ago
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The Mental Health Prediction System utilizes a Flask API to deploy the machine learning model, while a Spring Boot API handles user interactions, stores data, and sends personalized mental health predictions via email.
- Host: GitHub
- URL: https://github.com/wizardoftrap/mental-health-predicter
- Owner: wizardoftrap
- Created: 2025-03-01T13:18:18.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-01T13:26:34.000Z (over 1 year ago)
- Last Synced: 2025-03-01T14:27:34.654Z (over 1 year ago)
- Language: Java
- Size: 163 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 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.