This project focuses on analyzing factors influencing student performance using a machine learning pipeline. The implemented solution achieved an impressive R2 score of 88.03%, showcasing its effectiveness in predicting student outcomes.

• Features
• Installation
• Results
• Deployment
• Contributing
• License

• Machine Learning Pipeline: Designed and implemented a robust pipeline for analyzing student performance.
• Data Preprocessing and Feature Engineering: Conducted extensive data cleaning and feature engineering, reducing data noise by 20% and enhancing model stability.
• Ensemble of Algorithms: Utilized a diverse ensemble of machine learning algorithms, optimizing key parameters for a 12% performance improvement over baseline models.
• Scalability and Availability: Deployed the model on AWS Elastic Beanstalk, ensuring scalability and high availability for ongoing analysis of student performance factors.

1. Clone the repository:

   git clone https://github.com/prashver/model-deployment-end-to-end.git
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

The machine learning model achieved an impressive R2 score of 88.03%, indicating its high predictive power for student performance. Through extensive data preprocessing and feature engineering, data noise was reduced by 20%, contributing to the model's stability. Additionally, the use of a diverse ensemble of algorithms resulted in a 12% performance improvement over baseline models.

The model has been deployed on AWS Elastic Beanstalk to ensure scalability and high availability for ongoing analysis of student performance factors.

If you'd like to contribute to this project, please follow these steps:

1. Fork the repository
2. Create a new branch
3. Make your changes and commit them
4. Push your changes to your fork
5. Create a pull request

This project is licensed under the MIT License.

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