https://github.com/prashver/end-to-end-model-deployment-on-aws

Student Performance Analysis with Machine Learning analyzes factors impacting student outcomes using a robust machine learning pipeline. Achieving an impressive R2 score, it predicts student performance effectively. With extensive data preprocessing and deployment on AWS Elastic Beanstalk, it ensures scalability and high availability.
https://github.com/prashver/end-to-end-model-deployment-on-aws

amazon-web-services aws-elastic-beanstalk end-to-end-deployment flask machine-learning-algorithms matplotlib numpy pandas scikit-learn seaborn

Last synced: 3 days ago
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Student Performance Analysis with Machine Learning analyzes factors impacting student outcomes using a robust machine learning pipeline. Achieving an impressive R2 score, it predicts student performance effectively. With extensive data preprocessing and deployment on AWS Elastic Beanstalk, it ensures scalability and high availability.

• Host: GitHub
• URL: https://github.com/prashver/end-to-end-model-deployment-on-aws
• Owner: prashver
• Created: 2023-08-30T07:26:05.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-07T06:28:04.000Z (over 1 year ago)
• Last Synced: 2024-11-14T17:12:04.739Z (2 months ago)
• Topics: amazon-web-services, aws-elastic-beanstalk, end-to-end-deployment, flask, machine-learning-algorithms, matplotlib, numpy, pandas, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 2.31 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Student Performance Analysis with Machine Learning

## Overview

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.

## Table of Contents

- [Features](#features)

- [Installation](#installation)

- [Results](#results)

- [Deployment](#deployment)

- [Contributing](#contributing)

- [License](#license)

## Features

- **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.

## Installation

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

   ```

## Results

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.

## Deployment

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

## Contributing

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

## License

This project is licensed under the [MIT License](LICENSE).

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