semvec/pypi-packages huggingface dataset:
youtube-to-vlc 1.0
youtube_to_vlc is an YouTube Video Streaming library
Requires Python: >=3.6
Keywords: youtube to vlc,python vlc,youtube,vlc stream,yt to vlc pypi,youtube-to-vlc pypi,sijey,sijey-praveen,sijey praveen projects

<img src="https://raw.githubusercontent.com/sijey-praveen/youtube-to-vlc/Sijey/assets/vlc.png" height="80">
<p>YouTube To VLC</p>

- youtube_to_vlc is an YouTube Video Streaming library, written in Python, for human beings.
- Thanks To <a href="https://pywhatkit.herokuapp.com/">herokuapp.com</a>

<hr>

**How To Use**
```python
# import the library
from youtube_to_vlc import stream

# Enter the Youtube Video Name to Start stream in vlc.
stream.YouTubeVideo("Enter_The_YouTube_Video_Name")
```

<br>

# Requirement 
- <a href="https://python.org/downloads">Python 3.6 or Latest Version.</a>
- <a href="https://www.videolan.org/vlc/">VLC Media Player</a>

# Installation
```python
pip install youtube-to-vlc
```
steadynlp 0.0.2
A collection of simple NLP focused functions to support your main projects
Requires Python: >=3.8

# Hermione

A collection of NLP focused functions that supports my main projects

## Tasks:

### 1. Text Ranking
- Word Ranking: Input a text and get back a ranking of words based on Google's PageRank algorithm
- Sentence Ranking: Input a text and get back a ranking of sentences based on Google's PageRank algorithm
noisydatacleaner 1.0.5
Noise Detection and Label Correcting Package
Requires Python: >=3.8

# NoisyDataCleaner
Python classes that identify and correct/remove noise in datasets

These models leverage on monte carlo simulation to approximate the correctness of a given label. The correction of the label builds on from the noise detection model. 

<p align="center">
  <a href="LICENSE"><img src="https://img.shields.io/badge/License-MIT-brightgreen?style=plastic&logo=appveyor"/></a>
  <a href="https://pypi.org/project/noisydatacleaner/"><img src="https://img.shields.io/pypi/v/noisydatacleaner?style=plastic"/></a>
  <a href="https://pypi.org/project/noisydatacleaner/"><img src="https://img.shields.io/pypi/pyversions/noisydatacleaner?style=plastic"/></a>
</p>

## Install:
```bash
pip install noisydatacleaner
```

## Models: 

1. NoiseRemover
Identifies and then removes the noise from the dataset. Random Forest is used for smaller datasets as it yields better results. Whereas for larger datasets, k-Nearest Neighbors is much more efficient.

2. LabelClassificationCorrector
Corrects the labels for classification datasets. Instead of only using 1 model like `NoiseRemover`, this model uses 5 different models:
```python
models = {
   'Random Forest': RandomForestClassifier(n_estimators=128),
   'Extra Trees': ExtraTreesClassifier(n_estimators=128),
   'Linear Discriminant': LinearDiscriminantAnalysis(),
   'Logistic Regression': LogisticRegression(max_iter=128),
   'Neural Network': MLPClassifier(hidden_layer_sizes=(128,64,32))
}
```
All of which comes from the sklearn library