EduPredict: Machine Learning Insights for Academic Performance

Project Overview

EduPredict is a machine learning application designed to provide insights into academic performance. With an 89% success rate, this application leverages Python-based scripts to train, deploy, and evaluate machine learning models locally.

Key Achievements

• Developed and deployed a machine learning application with an 90% success rate.
• Streamlined local execution using Python commands and pre-configured scripts.
• Optimized performance through efficient code and dependency management.

Getting Started

Prerequisites

• Python 3.8 or higher
• Pip (Python package installer)

Setup

1. Clone the repository:

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

2. Create and activate 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. Train the model:

   python train.py

2. Run the application:

   python application.py

Usage

• Training Script: train.py trains the machine learning model and saves it to disk.
• Application Script: application.py runs the application and serves predictions.

Troubleshooting

• Dependency Issues:

  • Ensure all packages are installed correctly using pip install -r requirements.txt.
  • Check for any missing or outdated packages.

• Execution Errors:

  • Verify Python version compatibility.
  • Check logs for detailed error messages.

Contributing

Feel free to submit issues, pull requests, or suggestions. Please follow the project's code of conduct and contribution guidelines.