Metadata-Version: 2.4
Name: algomake
Version: 0.1.0
Summary: A custom machine learning library implemented from scratch.
Author-email: Arya Patil <patilarya3133@gmail.com>
License-Expression: MIT
Project-URL: Homepage, https://github.com/yourusername/algoforge
Project-URL: Bug Tracker, https://github.com/yourusername/algoforge/issues
Keywords: machine-learning,deep-learning,algorithms,from-scratch
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Development Status :: 3 - Alpha
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: numpy>=1.20.0
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: flake8>=4.0; extra == "dev"
Requires-Dist: black>=22.0; extra == "dev"
Requires-Dist: isort>=5.0; extra == "dev"
Requires-Dist: sphinx; extra == "dev"
Requires-Dist: sphinx_rtd_theme; extra == "dev"
Provides-Extra: docs
Requires-Dist: sphinx; extra == "docs"
Requires-Dist: sphinx_rtd_theme; extra == "docs"

# AlgoForge

A custom machine learning library built from scratch to deepen understanding of fundamental algorithms.

## Overview

AlgoForge is a personal project aimed at implementing various machine learning models and utility functions without relying heavily on established libraries like Scikit-learn or TensorFlow for core algorithm logic. The goal is to provide transparent and educational implementations of common machine learning concepts.

## Features

-   **Base Estimator:** A foundational `BaseEstimator` class for consistent API design.
-   **Gaussian Mixture Models (GMM):** A complete implementation of GMM using the Expectation-Maximization (EM) algorithm, optimized to run without `scipy` dependencies for its core components.
-   **K-Nearest Neighbors (KNN):** (Mention if implemented)
-   **Linear Models:** (Mention if implemented, e.g., Linear Regression, Logistic Regression)
-   **Preprocessing Tools:** (Mention if implemented, e.g., StandardScaler, MinMaxScaler)
-   **Evaluation Metrics:** (Mention if implemented, e.g., Accuracy, MSE)
-   **Modular Design:** Easy to extend with new algorithms.
-   **Comprehensive Testing:** Each component is rigorously tested using `pytest`.

## Installation

You can install AlgoForge directly from source:

```bash
git clone [https://github.com/yourusername/algoforge.git](https://github.com/yourusername/algoforge.git)
cd algoforge
pip install -e .
