Metadata-Version: 2.1
Name: M-O-Mini-SCIKIT-Learn-AI-Project
Version: 0.1.0
Summary: A mini implementation of scikit-learn with various machine learning models and utilities.
Home-page: https://github.com/mahatun/Mini-Scikit-Learn.git
Author: Maha,Oumaima
Author-email: maha.hanif@um6p.ma
License: UNKNOWN
Platform: UNKNOWN
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scikit-learn

# Lightweight Machine Learning Library

## Overview

This project aims to create a lightweight machine learning library inspired by the architecture of scikit-learn. The library provides users with powerful and easy-to-use tools for building machine learning models and analyzing data. It emphasizes modularity, ease of use, and efficient implementation.

## Features

- **Modular Design**: The library is designed with a modular architecture, separating different machine learning algorithms, utilities, and data processing functionalities into distinct modules or classes.
- **Preprocessing**: Handles data preprocessing tasks such as feature scaling, normalization, imputation of missing values, encoding categorical variables, and feature selection.
- **Supervised Learning**: Implementations of supervised learning algorithms for classification and regression tasks, including linear models, neighbors (KNN), Naive Bayes, Decision trees, Random forests, and Neural Networks.
- **Model Selection and Evaluation**: Provides utilities for model selection, hyperparameter tuning, and model evaluation. Includes tools for cross-validation, train-test split, grid search, and performance metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and mean squared error.
- **Ensemble Methods**: Implementations of bagging, boosting, and stacking techniques, along with ensemble models such as random forests, AdaBoost, and gradient boosting machines.
- **Neural Networks**: Basic neural network architectures, such as feedforward neural networks.
- **Utilities**: Data loading utilities, visualization tools, helper functions for common tasks, and integration with external libraries like NumPy and pandas.

## Installation

You can install the library using pip:

```bash
pip install lightweight-ml


