This is an End to End Automated Academic Performance Predictor Machine Learning Project

Explore it live at http://mlautomate-env.eba-saf64am8.us-east-1.elasticbeanstalk.com/

Explore it live at https://automatedperformance.azurewebsites.net/

Project: Automated Academic Performance Predictor

Objective: Develop a machine learning model predicting student marks using writing and reading scores, test preparation, race/ethnicity, and gender.

Key Features:

• CI/CD Integration for streamlined development and deployment.
• MLOps implementation for end-to-end automation in the ML lifecycle.
• Efficient model deployment with a focus on scalability.
• Ethical considerations in handling demographic data to prevent biases.
• Streamlined Deployment Processes on the Microsoft Azure Platform
• Documentation for transparency and collaboration.

Tools: CI/CD pipelines, MLOps practices, version control.

How to run?

STEPS:

Clone the repository

https://github.com/rkstu/AutomatedAcademicPerformancePredictor.git

STEP 01- Create a conda environment after opening the repository

conda create -p venv python=3.8 -y

conda activate venv

STEP 02- install the requirements

pip install -r requirements.txt

STEP 03- Finally run the following command

python application.py