Student Exam Performance Predictor

Overview

The Student Exam Performance Predictor is a Flask-based web application designed to predict students' exam performance based on various input features. It uses a trained machine learning model to make predictions and provides a user-friendly interface for input and results.

Features

• User-friendly interface: Provides a form for users to input student details and receive predictions.
• Predictive model: Uses a trained machine learning model for predictions.
• Responsive design: Mobile-friendly and visually appealing design.

Installation

1. Clone the Repository:

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

2. Set Up a Virtual Environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install Dependencies:

   pip install -r requirements.txt

Running the Application

1. Start the Flask Application:

   python app.py

   • The app will be available at http://127.0.0.1:5000/ by default.

2. Access the Application:

   • Open your browser and navigate to http://127.0.0.1:5000/ to view the home page.
   • Use the form to input student details and get predictions.

Project Details

• /artifacts/: Contains serialized objects like trained models and preprocessors.
• /catboost_info/: Contains information related to hyperparameters for CatBoost models.
• /notebook/: Includes Jupyter notebooks for data ingestion and exploration.
• /src/: Contains source code for the application, including prediction pipelines.
• /templates/: HTML templates for rendering web pages.
• app.py: Main Flask application script defining routes and handling requests.
• requirements.txt: Lists the Python dependencies required for the project.
• setup.py: Setup script for packaging and installing the application.

Contributing

Feel free to fork the repository and submit pull requests with improvements or fixes. Ensure that your code adheres to the existing style and passes all tests.

Acknowledgments

• Flask Documentation: Flask Documentation
• Scikit-learn Documentation: Scikit-learn Documentation
• CatBoost Documentation: CatBoost Documentation