https://github.com/cllspy/mlgo
DS from scratch with Golang
https://github.com/cllspy/mlgo
Last synced: 9 months ago
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DS from scratch with Golang
- Host: GitHub
- URL: https://github.com/cllspy/mlgo
- Owner: CllsPy
- Created: 2024-12-03T14:40:00.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-08T00:53:03.000Z (over 1 year ago)
- Last Synced: 2025-04-02T04:29:06.308Z (12 months ago)
- Language: Go
- Size: 4.88 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Science from Scratch with Golang
Welcome to the **Data Science from Scratch with Golang** repository! This project aims to implement essential data science concepts, algorithms, and workflows **from scratch** using the Go programming language (Golang). The repository serves as an educational tool for those interested in understanding the fundamental mechanics of data science without relying on high-level libraries.
## Objectives
- **Understand the Fundamentals**: Provide an in-depth understanding of key data science concepts by building them step-by-step from scratch.
- **Leverage Golang's Strengths**: Use Golang for its performance, simplicity, and strong support for concurrency.
- **Learn by Doing**: Offer an opportunity for developers to build algorithms and techniques manually, gaining a stronger grasp of the underlying principles.
## Features
- **Core Mathematical Foundations**:
- Linear algebra operations (vectors, matrices, dot products, etc.)
- Statistical computations (mean, variance, standard deviation, etc.)
- Probability functions and distributions
- **Data Preprocessing**:
- Data normalization and standardization
- Handling missing values
- Feature encoding
- **Algorithms and Models**:
- Regression models (e.g., Linear Regression)
- Classification models (e.g., Logistic Regression)
- Clustering algorithms (e.g., K-Means)
- Dimensionality reduction techniques (e.g., Principal Component Analysis)
- **Evaluation Metrics**:
- Accuracy, Precision, Recall, and F1 Score
- Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)
- Confusion Matrix
- **Visualization Utilities** (optional):
- Basic ASCII-based data visualizations (if feasible in Go).
## Directory Structure
```plaintext
├── algorithms/ # Core implementations of algorithms
│ ├── regression/
│ ├── clustering/
│ ├── preprocessing/
│ └── ...
├── data/ # Example datasets for testing algorithms
├── utils/ # Helper functions (e.g., file I/O, math operations)
├── tests/ # Unit tests for all modules
├── examples/ # End-to-end examples and use cases
├── go.mod # Go module file
├── go.sum # Dependency checksum file
└── README.md # Project overview and documentation
```
## Getting Started
### Prerequisites
- **Go (Golang)**: Install the latest version of Go from [golang.org](https://golang.org/dl/).
### Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/data-science-with-golang.git
cd data-science-with-golang
```
2. Install dependencies:
```bash
go mod tidy
```
### Running the Code
Run specific modules using the `go run` command. For example:
```bash
go run algorithms/regression/linear_regression.go
```
### Running Tests
Unit tests are available in the `tests` directory. Run all tests using:
```bash
go test ./...
```
## Contributing
Contributions are welcome! If you'd like to add new features, fix bugs, or improve documentation, please follow these steps:
1. Fork the repository.
2. Create a new branch:
```bash
git checkout -b feature-name
```
3. Commit your changes:
```bash
git commit -m "Add feature-name"
```
4. Push to your branch:
```bash
git push origin feature-name
```
5. Submit a pull request.
---
## Roadmap
- Implement more advanced machine learning algorithms (e.g., Support Vector Machines, Decision Trees).
- Add support for handling large datasets efficiently using Go's concurrency features.
- Expand documentation with tutorials and visual explanations.
- Explore integration with Go libraries for data visualization.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
## Acknowledgments
- Inspired by the book *Data Science from Scratch* by Joel Grus.
- Built with love for Golang and the data science community.
Feel free to explore the repository and deepen your understanding of data science through Go! If you encounter any issues or have suggestions, please open an issue in the repository. Happy coding!