semvec/pypi-packages huggingface dataset:
topsis-101803545 4.1.0
Python package to implement TOPSIS
License: MIT

This package is implementation of topsis technique of multi-criteria decision analysis.
This package will accept three parameters:
1. data.csv //file which contains the models and parameters
2. string of weights separated by commas(,)
3. string of requirements (+/-) separated by commas(,)
// important
install pandas,sys and math libraries before installing this
//
You can install this package using following command
pip install topsis-101803545
TOPSIS-VarunSachdeva-101803123 1.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

## 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.


## How to use this package:



TOPSIS-Varun-101803123 can be run as in the following example:

### In command prompt:

```bash
>> TOPSIS data.csv "1,1,2,1" "+,-,+,-" result.csv
```




## Sample Dataset

```python



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

```

## Output

```python
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1

```
powerspot 0.2
CLI for automated operations with Spotify
License: MIT
Requires Python: >=3.6

# powerspot

Command line interface and API in Python for advanced and automated operations with Spotify, based on the [click](https://github.com/pallets/click) and [spotipy](https://github.com/plamere/spotipy) packages.

## Installation instructions

This project uses Python >= 3.6.

```
pip install powerspot
```

## Usage

Examples of commands:
```bash
# get followed artists from Spotify profile and write them
powerspot artists write artists.json

# get new releases from those artists and save them in Spotify library
powerspot artists -f artists.json releases save

# show top tracks from recent activity
powerspot toptracks --term short show
```
Ishan 1.0.0
TOPSIS
License: MIT

# TOPSIS-Ishan-101803207
TOPSIS is an acronym that stands for "Technique of Order Preference Similarity to the Ideal Solution" and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those. 

## Installation 
'''pip install TOPSIS-Ishan-101803207==1.0.0'''

## License
Â©  2020 Ishan
This repository is licensed under the MIT license. 
See LICENSE for details
TOPSIS-DhairyaAhuja-101803720 1.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

## 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.


## How to use this package:



TOPSIS-DhairyaAhuja-101803720 can be run as in the following example:

### In command prompt:

```bash
>> TOPSIS data.csv "1,1,2,1" "+,-,+,-" result.csv
```




## Sample Dataset

```python



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

```

## Output

```python
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1

```
Topsis-Nitish-101803154 1.1.3
This package can be used to calculate the topsis score of multiple component data and rank them accordingly
License: MIT

# TOPSIS Package in Python

UCS538  Data Science Fundamentals
Assignment06 - TOPSIS

Submitted by: Nitish Jain

Roll no: 101803154

* * *

## Brief About TOPSIS

TOPSIS stands for â€˜Technique of Order Preference Similarity to the Ideal Solutionâ€™ and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those.

* * *

## Installation
#### Before running, make sure you have pandas installed on your system
```
>> pip install TOPSIS-Nitish-101803154
```

## Usage

```
>> python
>>>from topsis_analysis.topsispackage import topsis
>>>topsis("data.csv","1,1,1,2","+,+,-,+","output.csv")
```
## Sample Input

This input was used to test the module

<table><thead><tr><th>Model</th><th>Corr</th><th>Rseq</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>

## Output 


<table><thead><tr><th>Model</th><th align="right">Corr</th><th align="center">Rseq</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.639133</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.212592</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.407846</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.519153</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.828267</td><td>1.0</td></tr></tbody></table>


## License

Â© 2020 Nitish Jain

This repository is licensed under the MIT license. See LICENSE for details.