semvec/pypi-packages huggingface dataset:
Topsis-Gaurav-101803221 0.0.7
A Python package implementing TOPSIS technique.
License: MIT

#Topsis Package
It is a Python package to find out the best value among the different data using the mathematical calculations.
## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install this package.
bash
pip install Topsis-Gaurav-101803221
## About
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

To get started quickly, just use the following:

bash
from topis_pckg.topsis import topsis
topsis('inputfilename','Weights','Impacts','Outputfilename')

make ensure the weights and impacts should be in "" 

eg: "1,1,1,1" and "+,-,+,-"

## Pre-requisite
The data should be enclosed in the csv file. There must be more than 2 columns

## Result
the output(outputfilename)  is saved in the project folder with extra 2 columns with topsis score and rank.

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Priyansh 1.0.0
TOPSIS
License: MIT

# TOPSIS-Priyansh
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-priyansh-101803511==1.0.0'''

## License
Â©  2020 priyansh
This repository is licensed under the MIT license. 
See LICENSE for details
TOPSIS-Manpreet-101803562 0.1
We are calculating performances scores for each models which would be ranked and best model is considered
License: MIT

# TOPSIS-Python

**Topsis : UCS538**


Submitted By: **Manpreet Kaur - 101803562**

***

## What is TOPSIS

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
**S**olution (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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-Manpreet-101803562  can be run as in the following example:



### In Command Prompt
```
>> topsis data.csv "1,1,1,2" "+,+,-,+"
```
<br>

## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | 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

Weights (`w`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `I`.

<br>

The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.