https://github.com/nuraj250/ai-model-building-with-dl4j
A Java Spring Boot application that uses DL4J (Deeplearning4j) to build and train an AI model for predicting player suitability. It supports CRUD operations with MySQL and automatically replaces the trained model whenever performance data is updated.
https://github.com/nuraj250/ai-model-building-with-dl4j
ai backend deeplearning4j dl4j-library java java-ai machine-learning model-training mysql neural-network spring-boot
Last synced: 7 months ago
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A Java Spring Boot application that uses DL4J (Deeplearning4j) to build and train an AI model for predicting player suitability. It supports CRUD operations with MySQL and automatically replaces the trained model whenever performance data is updated.
- Host: GitHub
- URL: https://github.com/nuraj250/ai-model-building-with-dl4j
- Owner: Nuraj250
- Created: 2024-10-17T18:44:17.000Z (12 months ago)
- Default Branch: master
- Last Pushed: 2024-10-24T16:30:30.000Z (12 months ago)
- Last Synced: 2025-01-13T18:42:01.988Z (9 months ago)
- Topics: ai, backend, deeplearning4j, dl4j-library, java, java-ai, machine-learning, model-training, mysql, neural-network, spring-boot
- Language: Java
- Homepage:
- Size: 38.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# **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.
---
## **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
---
## **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
```
---
## **Setup and Installation**
### **Prerequisites**
- **Java 17** or higher installed
- **Maven** installed
- **MySQL** installed and running
- **Git** installed (for cloning the project)
---
### **Step 1: Clone the Repository**
```bash
git clone https://github.com/yourusername/ai-model-building-dl4j.git
cd ai-model-building-dl4j
```
---
### **Step 2: Set Up MySQL Database**
1. Start your MySQL server.
2. Create a new database:
```sql
CREATE DATABASE player_ai_db;
```
3. Configure the database credentials in **`src/main/resources/application.properties`**:
```properties
spring.datasource.url=jdbc:mysql://localhost:3306/player_ai_db?useSSL=false
spring.datasource.username=root
spring.datasource.password=yourpassword
```
---
### **Step 3: Build and Run the Application**
Use Maven to build and run the application:
```bash
mvn clean install
mvn spring-boot:run
```
---
### **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)**:
```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`
---
## **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**
```java
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();
```
---
## **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`.
---
## **Making Predictions**
You can use the trained model to predict player suitability programmatically:
```java
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"));
```
---
## **Contributing**
1. Fork the repository.
2. Create a new branch:
```bash
git checkout -b feature-branch
```
3. Make your changes and commit them:
```bash
git commit -m "Add new feature"
```
4. Push your changes:
```bash
git push origin feature-branch
```
5. Create a pull request.
---
## **License**
This project is licensed under the MIT License. See the **LICENSE** file for details.
---
## **Contact**
For questions or support, contact:
- **Your Name**: [your.email@example.com](mailto:your.email@example.com)
- **GitHub**: [https://github.com/yourusername](https://github.com/yourusername)
---