Student Performance Indicator with MLOPs

The "Student Performance Indicator with MLOps" project is a machine learning application designed to help educational institutions and educators assess and predict student academic performance. It includes data collection, preprocessing, and the development of machine learning models to forecast student outcomes. The project highlights the implementation of MLOps for Continous integration and Continous deployment (CI/CD) using GitHub Actions for model updates and features a user-friendly web application that offers performance predictions. This end-to-end solution promotes academic success and scalability while being hosted on Azure Cloud. It demonstrates the integration of machine learning and operational excellence in an educational context.

Features

Take User Input from the Web interface (Rest API).
Performs preprocessing on the raw data through data pipelines.
Pass the processed data to the ML model for Prediction.
Display the Predictions to the User on the Web page.

Getting Started

Prerequisites

Python 3.10
Virtualenv (optional but recommended)

Installation

Clone the repository:

git clone https://github.com/SinghJagpreet096/Studentperformanceindicator-with-mlops
cd Studentperformanceindicator-with-mlops

(Optional) Create and activate a virtual environment:
```
python3 -m venv venv
source venv/bin/activate
```
Install the required dependencies from requirements.txt:
```
pip install -r requirements.txt
```

Running the Application

Start the Flask application:

python application.py

Open your web browser and navigate to http://127.0.0.1:5000 to access the web interface.
Enter details in the Web interface to get the Predictions.

Usage

Enter details in the Given drop-down and text fields.
Click the Submit button to get the prediction.

Contributing

Contributions are welcome! If you'd like to contribute to this project, please follow the guidelines in CONTRIBUTING.md.

License

This project is licensed under the MIT License - see the LICENSE file for details.