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ML in Java Project Wiki
Welcome to the ML in Java project wiki! This space is dedicated to providing comprehensive documentation and guidance for developers and contributors working on this project. Below, you'll find key information about the project, including its goals, structure, usage, and how you can get involved.
## Project Overview The ML in Java project aims to provide a collection of machine learning algorithms and utilities implemented in Java. This project is designed to help developers build and evaluate machine learning models efficiently using Java.
## Getting Started Ensure you have the following installed: Java Development Kit (JDK), Integrated Development Environment (IDE) like IntelliJ IDEA, Eclipse, or VSCode, and Git. Clone the repository from GitHub, navigate to the project directory, and import it into your IDE.
The project includes various components such as:
DataLoader: Utility class for loading and preprocessing data.
MathUtils: Contains mathematical utility functions.
LinearRegression: Implementation of linear regression.
DecisionTree: Implementation of a decision tree classifier.
GradientDescent: Gradient descent optimization algorithm.
KMeansClustering: Implementation of the k-means clustering algorithm.
CrossValidation: Cross-validation utilities for model evaluation.
Metrics: Evaluation metrics for regression, classification, and clustering.
Each component of the project has specific methods for various machine learning tasks. For example, LinearRegression has methods to fit the model and predict target values. Refer to the documentation in the respective classes for detailed usage examples.
Contribution Guidelines We welcome contributions from the community. Fork the repository, create a new branch for your feature or bug fix, commit your changes, push to the branch, and create a pull request. Ensure your code follows the project's coding standards and includes appropriate tests and documentation.
The project is organized as follows:
src: Contains the source code.
test: Contains the test cases.
docs: Contains the documentation.
README.md: Project overview and setup instructions.
This project is licensed under the MIT License. See the LICENSE file in the repository for more details.
For any inquiries or collaborations, feel free to reach out via email at [email protected] or connect on LinkedIn at Aditya Kumar Mishra