From Data to Deployment : A Comprehensive MLOps Journey

Student Score Prediction Web Application

Overview

This project focused on developing a simple user interface to utilize a machine learning algorithm for predicting students' scores. The core objective was to demonstrate the entire process of building a Flask application that retrieves user input, deploys a machine learning model, calculates predictions using the model, and returns these predictions to the website for display.

Tools and Technologies

• Programming Languages: Python

• Frameworks and Libraries: Flask, scikit-learn, pandas, numpy

• Development Tools: Visual Studio Code, Git, GitHub

• Deployment Platforms: Heroku, AWS, GCP
  

Approach and Methodology

The project began with setting up a development environment and structuring the project to include components for data ingestion, transformation, model training, and a prediction pipeline. The Flask application was developed to handle 'GET' requests for form rendering and 'POST' requests for processing input data and returning predictions. An exploratory data analysis was performed to understand the dataset better, followed by model training where multiple algorithms were evaluated to select the best-performing model.

Results and Impact

The deployed application successfully demonstrates the ability to predict student scores based on input features. This project showcases the practical application of machine learning models and provides a template for developing similar applications. The impact of this work is twofold: it serves as an educational tool for understanding machine learning deployment and as a proof of concept for more complex predictive modeling applications.

Code and Repository Link

• GitHub Repository