Metadata-Version: 2.4
Name: sega_learn
Version: 0.1.12
Summary: Sega Learn is a Python package for machine learning and data science.
Author-email: Santiago Gonzalez <sega97@gmail.com>
License: MIT License
        
        Copyright (c) [2025] [Santiago Gonzalez]
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Project-URL: Homepage, https://github.com/SantiagoEnriqueGA/sega_learn
Project-URL: Repository, https://github.com/SantiagoEnriqueGA/sega_learn
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: statsmodels
Provides-Extra: vis
Requires-Dist: matplotlib; extra == "vis"
Requires-Dist: seaborn; extra == "vis"
Requires-Dist: tqdm; extra == "vis"
Requires-Dist: tabulate; extra == "vis"
Provides-Extra: accelerators
Requires-Dist: cupy-cuda12x>=13.4.0; extra == "accelerators"
Requires-Dist: numba>=0.60.0; extra == "accelerators"
Dynamic: license-file

# SEGA_LEARN

[![PyPI version](https://badge.fury.io/py/sega_learn.svg)](https://badge.fury.io/py/sega_learn)
[![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/SantiagoEnriqueGA/sega_learn/.github/workflows/core-tests.yml?branch=main)](https://github.com/SantiagoEnriqueGA/sega_learn/actions/workflows/core-tests.yml)
[![Python Versions](https://img.shields.io/pypi/pyversions/sega_learn.svg)](https://pypi.org/project/sega_learn/)
[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-blue.svg)](https://github.com/charliermarsh/ruff)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

SEGA_LEARN is a Python package for machine learning and data science, offering from-scratch implementations of various algorithms. It's designed primarily for educational purposes, allowing users to explore the mechanics of ML models, inspired by libraries like scikit-learn and PyTorch.

## Core Features
*   **Wide Range of Algorithms**: Implements models for:
    *   Automated Machine Learning (AutoML)
    *   Clustering (e.g., KMeans, DBSCAN)
    *   Linear Models (e.g., OLS, Ridge, Lasso, Logistic Regression, LDA, QDA)
    *   Nearest Neighbors (KNeighborsClassifier, KNeighborsRegressor)
    *   Neural Networks (flexible architecture with NumPy, Numba, and CuPy backends)
    *   Support Vector Machines (Linear, Generalized with Kernels, One-Class)
    *   Tree-based Models (Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Isolation Forest)
    *   Time Series Analysis (ARIMA, SARIMA, Decomposition, Exponential Smoothing)
*   **Pipelines**: Generic and forecasting pipelines for streamlining ML workflows.
*   **Utilities**: A rich set of tools for:
    *   Data preparation and preprocessing (scaling, encoding, imputation)
    *   Model evaluation metrics
    *   Model selection (Grid Search, Random Search)
    *   Data augmentation (SMOTE, over/under-sampling)
    *   Visualization and animation helpers

## Installation

You can install SEGA_LEARN directly from PyPI:

```bash
pip install sega_learn
```

## Quick Links

*   **Homepage & Repository**: [https://github.com/SantiagoEnriqueGA/sega_learn](https://github.com/SantiagoEnriqueGA/sega_learn)
*   **Full Documentation**: [https://santiagoenriquega.github.io/sega_learn/sega_learn](https://santiagoenriquega.github.io/sega_learn/sega_learn)
*   **Contributing/Development**: [https://github.com/SantiagoEnriqueGA/sega_learn/blob/main/DEVELOPMENT.md](https://github.com/SantiagoEnriqueGA/sega_learn/blob/main/DEVELOPMENT.md)

## License

This project is licensed under the MIT License - see the [LICENSE.txt](https://github.com/SantiagoEnriqueGA/sega_learn/blob/main/LICENSE.txt) file on GitHub for details.
