AI Model Building with DL4J

This project is a Java Spring Boot application that uses DL4J (Deeplearning4j) to train a neural network for predicting if a player is suitable or not for the team. The application supports CRUD operations on player performance data stored in a MySQL database and retrains the AI model automatically whenever the data changes.

Features

• CRUD Operations: Add, update, delete, and retrieve player performance data.
• AI Model: Uses a Neural Network (DL4J) for player suitability prediction.
• Automatic Model Retraining: The AI model retrains every time the performance data is modified.
• MySQL Integration: Stores player data in a relational database.
• REST API: Exposes endpoints for managing player data.

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Technologies Used

• Java 17
• Spring Boot (Web, Data JPA)
• DL4J (Deeplearning4j) for neural network implementation
• MySQL for data storage
• Lombok for reducing boilerplate code
• Maven for dependency management

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Project Structure

src/main/java/com/ictss
    ├── ai              # DL4J model logic
    ├── controller      # REST controllers
    ├── dto             # Data Transfer Objects (DTO)
    ├── model           # JPA entities
    ├── repository      # Spring Data JPA repositories
    └── service         # Service layer for business logic

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Setup and Installation

Prerequisites

• Java 17 or higher installed
• Maven installed
• MySQL installed and running
• Git installed (for cloning the project)

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Step 1: Clone the Repository

git clone https://github.com/yourusername/ai-model-building-dl4j.git
cd ai-model-building-dl4j

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Step 2: Set Up MySQL Database

1. Start your MySQL server.
2. Create a new database:

   CREATE DATABASE player_ai_db;

3. Configure the database credentials in src/main/resources/application.properties:

   spring.datasource.url=jdbc:mysql://localhost:3306/player_ai_db?useSSL=false
   spring.datasource.username=root
   spring.datasource.password=yourpassword

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Step 3: Build and Run the Application

Use Maven to build and run the application:

mvn clean install
mvn spring-boot:run

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Step 4: Test the API Endpoints

Use Postman or another tool to test the API.

1. Add Player Performance Data

• POST: http://localhost:8080/api/performance
• Body (JSON):

  {
      "average": 55.0,
      "strikeRate": 130.0,
      "bowlingAverage": 20.0,
      "economyRate": 4.0,
      "fieldingStats": 15,
      "label": 1
  }

2. Retrieve All Player Data

• GET: http://localhost:8080/api/performance

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How the AI Model Works

The project uses DL4J to build a neural network that predicts player suitability based on five performance metrics:
batting average, strike rate, bowling average, economy rate, and fielding stats.

Workflow

1. Data Collection: Player data is collected through the API and saved to MySQL.
2. Model Training: When data is added or updated, the neural network retrains to incorporate the changes.
3. Prediction: The trained model predicts if a player is suitable based on performance metrics.
4. Model Storage: The model is saved as player_model.zip and reloaded whenever required.

Model Configuration Example

MultiLayerConfiguration config = new NeuralNetConfiguration.Builder()
    .seed(123)
    .updater(new Adam(0.001))
    .list()
    .layer(0, new DenseLayer.Builder().nIn(5).nOut(64).activation(Activation.RELU).build())
    .layer(1, new DenseLayer.Builder().nIn(64).nOut(32).activation(Activation.RELU).build())
    .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.XENT)
        .nIn(32).nOut(1)
        .activation(Activation.SIGMOID)
        .build())
    .build();

MultiLayerNetwork model = new MultiLayerNetwork(config);
model.init();

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Retraining Process

1. When a player is added, updated, or deleted, the system retrieves the latest data.
2. The model re-trains using the new dataset.
3. The new model replaces the old one by saving it as player_model.zip.

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Making Predictions

You can use the trained model to predict player suitability programmatically:

float[] newPlayer = {50.0f, 120.0f, 25.0f, 4.2f, 14};
boolean isGoodPlayer = aiModel.predict(newPlayer);
System.out.println("Is the player suitable? " + (isGoodPlayer ? "Yes" : "No"));

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Contributing

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Push your changes:

   git push origin feature-branch

5. Create a pull request.

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License

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

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Contact

For questions or support, contact:

• Your Name: your.email@example.com
• GitHub: https://github.com/yourusername

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