semvec/pypi-packages huggingface dataset:
EmbeddedML 0.1.4
Optimized Machine Learning Library for Embedded Systems
License: MIT
Requires Python: >=3.7

# EmbeddedML

**EmbeddedML** is an optimized and lightweight machine learning library designed specifically for embedded systems (such as Raspberry Pi, Jetson Nano, Orange Pi, etc.).  
This library implements core ML algorithms with minimal dependencies, making it suitable for resource-constrained environments.

---

## 🚀 Features

- ✔️ Support for classic machine learning algorithms:
  - SVM (Support Vector Machines)
  - KNN (K-Nearest Neighbors)
  - Naive Bayes
  - Logistic Regression
  - Simple & Multiple Linear Regression
  - Polynomial Regression
  - K-Means Clustering
- 📊 Evaluation Metrics included (Accuracy, Precision, Recall, etc.)
- 🧹 Preprocessing utilities
- 🐍 Pure Python implementation – no external dependencies
- 🪶 Lightweight and fast

---

## 📦 Installation

Install the package via `pip`:

```bash
pip install EmbeddedML
molactivity 3.0
Molecular activity prediction using transformer neural networks with 5 operational modes
License: MIT
Requires Python: >=3.8
Keywords: molecular-activity, machine-learning, transformer, neural-networks, natural-products, drug-discovery, cheminformatics, bioinformatics, SMILES, molecular-modeling, deep-learning, pharmaceutical, chemical-analysis, molecular-descriptors, QSAR, drug-development

# Molactivity 3.0

![License](https://img.shields.io/badge/license-MIT-blue.svg)
![Python](https://img.shields.io/badge/python-3.8%2B-blue.svg)
![Status](https://img.shields.io/badge/status-stable-green.svg)
![PyPI](https://img.shields.io/badge/pypi-v3.0-blue.svg)

**Advanced Molecular Activity Prediction Toolkit** 
- Multi-mode molecular property prediction platform based on Transformer neural networks.

1. Quick Start

```bash
pip install molactivity[all]
```

2. Full Documentation

For complete installation guides, usage examples, scripts, and documentation, please visit our official website:

**   molactivity.com (http://molactivity.com)   **

3. Five Modes Available

- **Standard Mode**: Pure Python implementation
- **Fast Mode**: NumPy optimization  
- **Rocket Mode**: PyTorch deep learning
- **Image Mode**: Molecular image processing
- **Tools Mode**: Analysis utilities