m-o-mini-scikit-learn-ai-project 0.1.1

A mini implementation of scikit-learn with various machine learning models and utilities.

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:

pip install lightweight-ml