semvec/pypi-packages huggingface dataset:
spotrend 0.0.2
Fundament package for Spotify data integration using Spotipy and Spotify API.
Requires Python: >=3.7

<div align="center">
  <img width="600px" src="https://user-images.githubusercontent.com/121885783/212549809-136e2c72-aae6-42e8-bfb6-52d714a4c589.png" />
</div>

[![Language - Python](https://img.shields.io/static/v1?label=Python&message=3.8&color=blue&logo=github)](https://github.com/mase-git/spotrends "Go to GitHub repo")
[![issues - spotrends](https://img.shields.io/github/issues/spotrend/spotrend)](https://github.com/spotrend/spotrend/issues)
[![License](https://img.shields.io/badge/License-GNU_3.0-blue)](#license)
# Spotrend

## Documentation
## Installation
## Quick Start


## Acknowledgments

We would like to thank the following open-source projects for their contributions to this project:

- [Spotify API](https://developer.spotify.com/documentation/web-api/)
- [Spotipy](https://github.com/spotipy-dev/spotipy)

We would also like to thank Spotify for providing access to their API and for their support throughout the development of related projects. 

## License

This project is licensed under the GPL 3.0 license. See [LICENSE](https://github.com/mase-git/spotrends/blob/main/LICENSE) for more information.

The GPL 3.0 license is a permissive license that allows for the use and modification of the licensed software, as long as the original copyright notice and license are included in any copies or derivative works. It is a good choice for open-source projects that want to allow others to use and modify their code without imposing too many restrictions.
TOPSIS-SahilRohilla-102183056 1.0.4
A Topsis package that takes inputs as CSV and generates scores in results CSV!

**TOPSIS_101803318**

TOPSIS method for multiple-criteria decision making (MCDM)

**What is TOPSIS?**

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.


## ðŸ”— Usage
topsis data.csv "1,1,1,1" "+,+,-,+" result.csv

## ðŸ”— Input File(data.csv)

Model	Correlation	R2	RMSE	Accuracy

M1	0.79	0.62	1.25	60.89

M2	0.66	0.44	2.89	63.07

M3	0.56	0.31	1.57	62.87

M4	0.82	0.67	2.68	70.19

M5	0.75	0.56	1.3	80.39

## ðŸ”— Output File(result.csv)

Model	Correlation	R2	RMSE	Accuracy	Topsis_score	Rank

M1	0.79	0.62	1.25	60.89	0.7722	2

M2	0.66	0.44	2.89	63.07	0.2255	5

M3	0.56	0.31	1.57	62.87	0.4388	4

M4	0.82	0.67	2.68	70.19	0.5238	3

M5	0.75	0.56	1.3	80.39	0.8113	1
spotrends 0.0.1
A Spotify track loader package using Spotipy API
Requires Python: >=3.7

<div align="center">
  <img width="600px" src="https://user-images.githubusercontent.com/121885783/212549809-136e2c72-aae6-42e8-bfb6-52d714a4c589.png" />
</div>

[![Language - Python](https://img.shields.io/static/v1?label=Python&message=3.8&color=blue&logo=github)](https://github.com/mase-git/spotrends "Go to GitHub repo")
[![issues - spotrends](https://img.shields.io/github/issues/spotrend/spotrend)](https://github.com/spotrend/spotrend/issues)
[![License](https://img.shields.io/badge/License-GNU_3.0-blue)](#license)
# Spotrend

## Documentation
## Installation
## Quick Start


## Acknowledgments

We would like to thank the following open-source projects for their contributions to this project:

- [Spotify API](https://developer.spotify.com/documentation/web-api/)
- [Spotipy](https://github.com/spotipy-dev/spotipy)

We would also like to thank Spotify for providing access to their API and for their support throughout the development of related projects. 

## License

This project is licensed under the GPL 3.0 license. See [LICENSE](https://github.com/mase-git/spotrends/blob/main/LICENSE) for more information.

The GPL 3.0 license is a permissive license that allows for the use and modification of the licensed software, as long as the original copyright notice and license are included in any copies or derivative works. It is a good choice for open-source projects that want to allow others to use and modify their code without imposing too many restrictions.