https://github.com/devrihan/self-analysis_mental_health-model

https://github.com/devrihan/self-analysis_mental_health-model

Last synced: 8 months ago
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• Host: GitHub
• URL: https://github.com/devrihan/self-analysis_mental_health-model
• Owner: devrihan
• Created: 2025-02-07T07:48:22.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-07T08:21:37.000Z (over 1 year ago)
• Last Synced: 2025-02-07T09:26:24.667Z (over 1 year ago)
• Language: Jupyter Notebook
• Size: 736 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Self Analysis Mental health model

## Overview

This project is a **mental health prediction system** that leverages **machine learning** to assess whether an individual should seek treatment for mental health conditions. The system features:

- A **Streamlit-based UI** for easy user interaction.

- A **machine learning model** trained on mental health datasets.

- **AI-powered conversational chatbots** for additional insights. Users can converse with AI models to receive guidance and support.

- **Image analysis** for physical health-related assessments.

## Features

### 1. Mental Health Prediction Model

- Predicts whether treatment is recommended based on user input.

- Uses a **pre-trained model** for inference.

### 2. Additional AI Features

- **Gemini AI Chatbot**: Provides mental health insights based on user queries.

- **Mental Health Chatbot**: A conversational AI tool that allows users to discuss mental well-being, ask questions, and receive AI-generated responses in real-time.

- **Image Analysis**: Allows users to upload images for AI-driven assessments related to physical health conditions.

## Dataset Preprocessing Steps

1. **Data Cleaning**: Removed null values, handled missing data.

2. **Feature Encoding**: Converted categorical features to numerical values using label encoding.

3. **Feature Selection**: Selected relevant features impacting mental health.

4. **Normalization**: Scaled data for optimal model performance.

## Model Selection Rationale

- **Algorithm**: Chose a suitable classifier based on performance metrics.

- **Evaluation Metrics**: Measured accuracy, precision, and recall.

- **Optimization**: Fine-tuned hyperparameters for improved results.

## How to Run the Inference Script

### Prerequisites

Ensure you have the necessary dependencies installed:

```bash

pip install -r requirements.txt

```

### Steps

1. Run the script:

   ```bash

   python predict_mental_health.py

   ```

2. Input the required details when prompted.

3. View the prediction output, which suggests whether treatment is recommended.

## UI Usage Instructions

### **Streamlit Interface**

#### **How to Use:**

1. **Install Dependencies:**

   ```bash

   pip install -r requirements.txt

   ```

   Also, set up the **Gemini API key** inside `api_key.py`.

2. **Launch the Streamlit App:**

   ```bash

   streamlit run app.py

   ```

3. **Fill in the following details:**

   - Name

   - Age

   - Gender

   - Employment Status

   - Other relevant inputs like `family_history`, `remote_work`, and `mental_health_consequence`.

4. **Click "Predict"** to get the results.

5. **The system will process the inputs and display:**

   - Whether treatment is recommended or not.

   - Additional insights generated via **Gemini AI chatbot**.

🔹 **Note:** Before running the application, ensure you have set up your **Gemini API Key** inside `api_key.py` to enable AI-based insights.