semvec/pypi-packages huggingface dataset:
rich-monokai 1.3.0
A Monokai theme for the Textualize/rich Python package.
License: MIT
Requires Python: >=3.0
Keywords: rich, monokai

# rich-monokai

A Monokai theme for the [Textualize/rich](https://github.com/Textualize/rich) Python package.

## Installation

```shell
$ pip install rich-monokai
```
pymine-edu 0.1.0
An interpretable, transparent, and educational data mining library built from scratch in pure Python.
License: MIT
Requires Python: >=3.7
Keywords: data mining, machine learning, education, explainable ai, classification, clustering, association rules, preprocessing, python from scratch, interpretable ml, pure python

# PyMine

**Educational, Explainable, and Pure-Python Data Mining Library**

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### 📘 Description

**PyMine** is a lightweight, dependency-free Python library built for learning, teaching, and exploring core data mining algorithms. Developed entirely from scratch using pure Python, PyMine prioritizes **transparency**, **explainability**, and **educational clarity** — making it a perfect fit for students, educators, and enthusiasts who want to understand *how* algorithms work under the hood.

---

### ⚙️ Features

- 📊 **Classification**: Decision Tree, Naive Bayes, Logistic Regression, K-Nearest Neighbors  
- 🔍 **Clustering**: K-Means, DBSCAN, Hierarchical Clustering  
- 🔁 **Association Rule Mining**: Apriori algorithm with support thresholds  
- ⚠️ **Anomaly Detection**: Z-Score, Local Outlier Factor  
- 🛠 **Preprocessing**: Scalers, Imputers, Label and One-Hot Encoders  
- 📏 **Evaluation Metrics**: Accuracy, Precision, Recall, F1, Confusion Matrix, Silhouette Score  
- 💬 **Explainability**: All models support `.explain()` and "what-if" transformation introspection  
- 🔬 **No Dependencies**: Pure Python. No NumPy, pandas, or scikit-learn required

---

### 📦 Installation

```bash
pip install pymine-fashjr