semvec/pypi-packages huggingface dataset:
privatebin 0.3.0
Python library for interacting with PrivateBin's v2 API (PrivateBin >= 1.3) to create, retrieve, and delete encrypted pastes.
Requires Python: >=3.10
Keywords: paste, pastebin, privatebin

# privatebin

[![PyPI - Version](https://img.shields.io/pypi/v/privatebin?link=https%3A%2F%2Fpypi.org%2Fproject%2Fprivatebin%2F)](https://pypi.org/project/privatebin/)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/privatebin)
![License](https://img.shields.io/github/license/Ravencentric/privatebin)
![PyPI - Types](https://img.shields.io/pypi/types/privatebin)

![GitHub Build Workflow Status](https://img.shields.io/github/actions/workflow/status/Ravencentric/privatebin/release.yml)
![GitHub Tests Workflow Status](https://img.shields.io/github/actions/workflow/status/ravencentric/privatebin/tests.yml?label=tests)
[![codecov](https://codecov.io/gh/Ravencentric/privatebin/graph/badge.svg?token=L1ZPQCVNDG)](https://codecov.io/gh/Ravencentric/privatebin)

Python library for interacting with PrivateBin's v2 API (PrivateBin >= 1.3) to create, retrieve, and delete encrypted pastes.

## Installation

`privatebin` is available on [PyPI](https://pypi.org/project/privatebin/), so you can simply use [pip](https://github.com/pypa/pip) to install it.

1. To install the core library:

    ```sh
    pip install privatebin
    ```

2. To install the CLI:

    - With [`pipx`](https://pipx.pypa.io/stable/) or [`uv`](https://docs.astral.sh/uv/guides/tools/#installing-tools) (recommended)

        ```sh
        pipx install "privatebin[cli]"
        ```
        ```sh
        uv tool install "privatebin[cli]"
        ```

    - With [`pip`](https://pip.pypa.io/en/stable/installation/)

        ```sh
        pip install "privatebin[cli]"
        ```

## Docs

Checkout the [quick start page](https://ravencentric.cc/privatebin/quick-start/) and the [API reference](https://ravencentric.cc/privatebin/api-reference/client/).

## License

Distributed under the [MIT](https://choosealicense.com/licenses/mit/) License. See [LICENSE](https://github.com/Ravencentric/privatebin/blob/main/LICENSE) for more information.
ml-model-handler 0.1.0
A simple library to train, manage, and use multiple ML models easily.
Requires Python: >=3.7

# ml_model_handler

A lightweight Python package for training, managing, and predicting with multiple machine learning models.  
Supports classical ML algorithms, pipelines, ensemble learning (Voting), and user-defined custom estimators.  

---

## Features
- Preconfigured ML models: Logistic Regression, KNN, Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, XGBoost, SVM, SGD.  
- Voting Classifier support (soft/hard voting).  
- Plug-and-play with **custom estimators**.  
- Built-in pipelines with `StandardScaler` where useful.  
- Easy single prediction and batch predictions.  
- Probability predictions (`predict_proba`) when available.  

---

## Installation
```
pip install ml-model-handler
```

---

## Quick Start

```python
import pandas as pd
from ml_model_handler import MLModelHandler

# Example dataset
X = pd.DataFrame({
    "feature1": [1, 2, 3, 4],
    "feature2": [10, 20, 30, 40]
})
y = [0, 1, 0, 1]

# Initialize handler
model_handler = MLModelHandler()

# Train default models
model_handler.train_models(X, y)

# Single prediction
print(model_handler.predict("logistic", [5, 50]))

# Batch prediction
batch_features = [
    [6, 60],
    [7, 70]
]
model_handler.batch_predict("knn", batch_features)

# Probability prediction
print(model_handler.predict_proba("logistic", [5, 50]))
```

---

## Voting Classifier

The `voting` estimator combines predictions from multiple trained models (soft or hard voting).

```python
# Use Logistic, KNN and Voting
model_handler.train_models(X, y, estimators=["logistic", "knn"], voting_type="soft")

print(model_handler.predict("voting", [7, 70]))
```

⚠️ If fewer than 2 base models are given with `voting`, it falls back to all trained models with a warning.

---

## Custom Estimators

You can extend with your own models:

```python
from sklearn.linear_model import RidgeClassifier

custom = {
    "ridge": RidgeClassifier()
}

model_handler.train_models(X, y, estimators=["ridge"])
print(model_handler.predict("ridge", [8, 80]))
```

---

## Project Structure
```
ml_model_handler/
 ├── __init__.py
 └── main.py          # MLModelHandler class
README.md
setup.py
```

---

## Contributing
Contributions are welcome!  
- Fork the repo  
- Create a feature branch  
- Submit a pull request

---

## License
[MIT License](https://github.com/Rucha-Ambaliya/ml_model_handler/blob/main/LICENSE)