semvec/pypi-packages huggingface dataset:
Topsis-IshikaGoel-101803407 0.0.1
It is method to find out best value among different data using mathematical calculation.
Keywords: topsis

It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.

Change Log
==========

0.0.1(13/11/2020)
------------------
-First Release
Topsis-Ria-101803258 0.0.6
it is package which tells you best value based on the data by applying accurate mathematics functions
Requires Python: >=3.6

topsis\_pckg
============

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_Ria-101803258
```

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.

Please make sure to update tests as appropriate:
https://github.com/ria-2511/topsis_pckg \#\# 
License
[MIT](https://choosealicense.com/licenses/mit/)