Make your ML solid!
Project description
Table of Contents
About The Project
Solidipy-MIPT is a Python library designed to provide a solid foundation for machine learning tasks. It includes various machine learning algorithms such as Weighted k-nearest neighbors (WKNN) and regressions, along with evaluation metrics to assess model performance.
Built With
Major frameworks/libraries used to bootstrap solidipy-mipt.
Getting Started
To get a local copy up and running follow these simple example steps.
Prerequisites
Before installing Solidipy-MIPT make sure you have last version of Python3 and pip.
Installation
You can install solidipy-mipt using pip:
pip install solidipy-mipt
Usage
Simple Weighted KNN example
import numpy as np
from solidipy_mipt import accuracy
from solidipy_mipt.algorithms import WKNN
X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
y = np.array([0, 1, 0, 1])
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_ratio=0.6, shuffle=True
)
wknn = WKNN()
wknn.fit(X_train, y_train)
prediction = wknn.predict(X_test)
print(accuracy(prediction, y_test))
For more examples, please refer to the solidipy_mipt examples
Contributing
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
License
Distributed under the MIT License. See LICENSE.txt
for more information.
Contact
Matvei Gorskii - t.me/Kynemallv - matveygor41@gmail.com
Project Link: https://github.com/your_username/repo_name
Project details
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