Baccalauréat Score Prediction RMI Application

This repository contains a Java RMI application designed to predict Baccalauréat (BAC) scores. It utilizes a client-server architecture to facilitate seamless communication and computation for BAC score predictions.

Structure

The project is divided into two main components:

• client: The client module is responsible for the user interface and interaction. It sends data to the server for processing and displays prediction results.

• server: The server module handles the core computation and prediction logic. It receives data from the client, processes it, and sends back the prediction results.

Prerequisites

• Java JDK 17 or higher
• Apache Maven (for dependency management and building the project)

Setup

1. Clone the repository to your local machine.
2. Navigate to the project's root directory.
3. Build the project using Maven:

Database Setup

1. Clone the repository to your local machine.

2. Install MySQL and ensure it's running on your system.

3. Create a new MySQL database for the application.

4. Navigate to the server directory and open the DatabaseUtil class.

5. Configure your database connection details (username, password, database URL) in the DatabaseUtil class.

6. Execute the following SQL command to create the StudentRecords table within your MySQL database:

   CREATE TABLE StudentRecords (
       id INT AUTO_INCREMENT PRIMARY KEY,
       grade1 FLOAT,
       grade2 FLOAT,
       grade3 FLOAT,
       bacGrade FLOAT,
       schoolName varchar(255),
       createdAt datetime DEFAULT CURRENT_TIMESTAMP
   );

Running the Application

To run the server component:

1. Navigate to the server directory.
2. Execute the server application:

To run the client component:

1. Navigate to the client directory.
2. Execute the client application:

Features

• Data Upload: Users can upload student records for analysis.
• Score Prediction: The application predicts BAC scores based on the provided data.
• Model Training: New prediction models can be trained with updated data sets.

Architecture

The application follows a Remote Method Invocation (RMI) pattern, allowing the client and server to communicate remotely with the following interfaces:

• DataUploadService
• PredictionService
• TrainingService