machinelite 0.0.2

Sci-Lite is Light version of Supervised Machine Learning Model

MachineLite

MachineLite provides a streamlined solution for evaluating various supervised machine learning algorithms, simplifying the data scientist's workflow by automating model integration and evaluation. With just a few lines of code, SciLite enables quick access to essential machine learning evaluations, saving valuable time and effort.

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Installation

To install the package, run:

pip install machinelite

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Usage

Basic example of how to use the package in Python:

from machinelite.univariate.regression import Regression as reg
result = reg(X_train, X_test, y_train, y_test)
print(result)

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Features

• One-Click Model Integration: Supports easy integration and evaluation of multiple supervised machine learning models, whether for regression or classification.
• Efficient Model Evaluation: Simplifies machine learning model evaluations with standardized output, aiding in faster analysis and comparisons.

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Contributing

Contributions to MachineLite are warmly welcomed! If you'd like to contribute, please fork the repository and submit a pull request or open an issue to discuss improvements.

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Documentation

MachineLite: View Here

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License

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Contact

For any questions or issues, feel free to reach out to:

• Author: Ayush Jain Sparsh
• Email: [e-mail]

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