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

Project description

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

Project details


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m_o_mini_scikit_learn_ai_project-0.1.0.tar.gz (15.0 kB view hashes)

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