🌟 Students Performance Prediction

🌐 Demo

🔗 Live Demo

📖 Overview

This project is designed to provide insights and predictions using a comprehensive pipeline for data processing, analysis, and visualization. The repository includes components for web interaction, machine learning pipelines, and detailed visualizations to explore data trends and patterns.

✨ Features

• 📊 Data Analysis and Visualization: Prebuilt plots in the Visualization_images directory showcase key data trends.
• 🤖 Machine Learning Pipelines: Scripts for training and prediction workflows are available in the /src directory.
• 🌐 Web Interface: A simple HTML interface is included in the templates directory.
• 🐳 Containerization: Docker support for seamless deployment.

🗂️ Directory Structure

/                           # Root directory
|-- artifacts/              # Models, Test and Train data
|-- logs/                   # Logs of project
|-- data/                   # Datasets
|-- mlproject.egg-info/     # Whole project avaliable as a package      
|-- notebook/               # Jupyter notebooks
|-- LICENSE                 # License file
|-- README.md               # Project documentation
|-- requirements.txt        # Python dependencies
|-- Visualization_images/   # Plots and visualizations
|-- templates/              # HTML templates for the web interface
|-- src/                    # Source code
|   |-- components/         # Modular components for tasks
|   |-- pipeline/           # Training and prediction pipelines
|   |-- utils.py            # Utility functions
|-- Dockerfile              # Docker configuration
|-- app.py                  # Main application script
|-- setup.py                # Package setup file

🚀 Getting Started

✅ Prerequisites

• 🐍 Python 3.8+
• 🐳 Docker (optional, for containerized deployment)

🛠️ Installation

1. Clone the repository:

   git clone https://github.com/username/repository-name.git
   cd repository-name

2. Install dependencies:

   pip install -r requirements.txt

▶️ Running the Application

To start the application locally:

python app.py

🐳 Docker Deployment

1. Build the Docker image:

   docker build -t project-name .

2. Run the container:

   docker run -p 5000:5000 project-name

📈 Visualizations

Visualizations are pre-generated and stored in the Visualization_images directory. These include:

• 📊 Score distributions
• 🔥 Correlation heatmaps
• 📦 Boxplots of scores by category

🛠️ Development

📂 Code Structure

• src: Contains the core logic of the application, including error handling (exception.py), logging (logger.py), and utility functions.
• Pipelines: Training and prediction workflows are implemented in predict_pipeline.py.

🧪 Testing

Unit tests can be added in the tests directory (not present but recommended).

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature:

   git checkout -b feature-name

3. Commit your changes and push the branch:

   git push origin feature-name

4. Submit a pull request.

📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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Feel free to explore the code and provide feedback! 😊