The Student Score Prediction Machine Learning Project is a comprehensive end-to-end machine learning application that predicts the scores of students based on various features and attributes. This project utilizes a range of classical machine learning algorithms to build predictive models, deploys them using AWS Elastic Beanstalk, and provides a user-friendly interface through a Flask server.

The project incorporates a variety of machine learning models, including Random Forest, Decision Tree, Gradient Boosting, Linear Regression, XGBRegressor, CatBoostRegressor, and AdaBoostRegressor. Each model is fine-tuned with specific hyperparameters to achieve optimal predictive performance.

Hyperparameter optimization is a critical aspect of this project. It explores different hyperparameter settings for each model to enhance predictive accuracy and generalization.

The application is deployed on the AWS cloud platform using Elastic Beanstalk, ensuring scalability and reliability. This allows users to access the prediction service effortlessly.

A Flask server acts as the front end of the application, providing a user-friendly interface for inputting student data and obtaining score predictions. Users can interact with the predictive models seamlessly through this interface.

This project can be used for educational purposes, helping educators and students gain insights into factors that influence academic performance. It can assist in identifying areas where intervention may be needed to improve student outcomes.