https://github.com/farzanmrz/native-ml-algorithms

https://github.com/farzanmrz/native-ml-algorithms

Last synced: 18 days ago
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• Host: GitHub
• URL: https://github.com/farzanmrz/native-ml-algorithms
• Owner: Farzanmrz
• License: mit
• Created: 2024-06-19T16:53:28.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-12-02T13:27:32.000Z (about 1 month ago)
• Last Synced: 2024-12-02T14:29:43.608Z (about 1 month ago)
• Language: Python
• Size: 5.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Native Machine Learning Algorithms

This project contains native implementations of fundamental machine learning algorithms and demonstrates their application on various datasets. The goal is to provide a hands-on experience with core ML algorithms without the use of high-level libraries such as scikit-learn or TensorFlow.

## Structure

- `/models`: Contains the implementation of each machine learning algorithm.

- `/evaluation`: Contains scripts for evaluating the algorithms on different datasets.

- `/utilities`: Includes utility functions shared across different evaluation scripts.

- `/data`: Includes datasets used for various analyses.

## Algorithms

Each algorithm is implemented in a modular way to showcase the core mathematical concepts and their application. The algorithms are all stored in the `models.py` file at root. The following algorithms are included:

### PCA (Principal Component Analysis)

**Evaluation**: `evaluation/pca.py`  

**Description**: PCA is used for dimensionality reduction by projecting data onto a lower-dimensional space while preserving as much variance as possible.

### Closed-form Linear Regression

**Evaluation**: `evaluation/linreg.py`  

**Description**: Linear regression is used for predicting a continuous target variable based on one or more predictor variables using a closed-form solution.

### Logistic Regression

**Evaluation**: `evaluation/logreg.py`  

**Description**: Logistic regression is used for binary classification tasks by estimating the probability that a given input belongs to a certain class.

### LDA (Linear Discriminant Analysis)

**Evaluation**: `evaluation/lda.py`  

**Description**: LDA is used for classification and dimensionality reduction by finding the linear combinations of features that best separate different classes.

### Decision Tree

**File**: `models/dtl.py`  

**Evaluation**: `evaluation/dt.py`  

**Description**: Decision trees are used for classification and regression tasks by recursively splitting the data into subsets based on feature values.

## Future Work

- Implement more complex ML algorithms.

- Include more examples and tutorials on how to use the algorithms for classification, regression, and other tasks.

- Enhance the project with additional features and optimizations.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contact

* Farzan Mirza: [[email protected]](mailto:[email protected]) | [LinkedIn](https://www.linkedin.com/in/farzan-mirza13/)