InnoML 0.1.0

A collection of machine learning models implemented from scratch

InnoML

Overview

This package is a custom-built implementation of several foundational machine learning algorithms developed entirely from scratch, without reliance on libraries like scikit-learn. It provides an educational and practical resource for understanding and applying machine learning techniques at a granular level.

Implemented Algorithms

1. Linear Regression
2. Multiple Linear Regression
3. Logistic Regression
4. K-Nearest Neighbors (KNN)
5. Decision Tree
6. Random Forest

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Features

• Custom Implementation: All algorithms are implemented from first principles, ensuring transparency and understanding of the underlying mechanics.
• Versatile: Supports both regression and classification tasks.
• No External Dependencies: Built without using pre-existing ML libraries, providing full control over model behavior and implementation details.
• Educational Resource: Ideal for those who want to learn the inner workings of machine learning algorithms.

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Installation

Clone the repository to your local machine:

git clone https://github.com/SriramR123/InnoML.git
cd InnoML

No additional libraries are required beyond Python's standard libraries.

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Usage

Example: Linear Regression

from InnoML.linear_regression import LinearRegression

# Example data
X = [[1], [2], [3], [4], [5]]
y = [2, 4, 6, 8, 10]

# Initialize and fit the model
model = LinearRegression()
model.fit(X, y)

# Make predictions
predictions = model.predict([[6], [7]])
print(predictions)  # Output: [12, 14]

Available Modules

Each algorithm is implemented in a separate module for clarity and modularity:

• linear_regression.py: Implementation of simple and multiple linear regression.
• logistic_regression.py: Logistic regression for binary classification.
• knn.py: K-Nearest Neighbors for classification and regression.
• decision_tree.py: Decision tree for classification and regression.
• random_forest.py: Ensemble learning using Random Forest.

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Contributing

Contributions are welcome! If you find a bug or have a feature request, feel free to open an issue or submit a pull request.

Steps to Contribute:

1. Fork the repository.
2. Create a new branch: git checkout -b feature-name.
3. Commit your changes: git commit -m 'Add feature name'.
4. Push to the branch: git push origin feature-name.
5. Open 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 feedback, feel free to reach out:

• Email: [e-mail]
• GitHub: SriramR123

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Acknowledgements

This project is inspired by the desire to understand and implement machine learning algorithms at a foundational level. Special thanks to the open-source community for their resources and support.