Student Performance End-to-End Project

This project aims to analyze and predict student performance using a comprehensive end-to-end pipeline. It includes data preprocessing, model training, deployment, and continuous integration/continuous deployment (CI/CD) using Docker. The project is deployed on both AWS and Azure cloud platforms.

Table of Contents

• Introduction
• Project Structure
• Prerequisites
• Installation
• Usage
• CI/CD Pipeline
• Deployment
• Contributing
• License

Introduction

The student performance end-to-end project leverages machine learning techniques to predict student performance based on various factors. The project involves the following key steps:

1. Data collection: Gather student performance data from reliable sources.
2. Data preprocessing: Clean and preprocess the data to prepare it for model training.
3. Model training: Develop and train a machine learning model on the preprocessed data.
4. Model evaluation: Assess the model's performance using appropriate metrics.
5. Deployment: Deploy the model on both AWS and Azure cloud platforms.
6. CI/CD pipeline: Implement a CI/CD pipeline using Docker to automate the deployment process.

Project Structure

The project structure is as follows: