Metadata-Version: 2.1
Name: topsis-Kushagra-101917112
Version: 0.1
Summary: A Python package implementing TOPSIS technique.
Author: Kushagra Rastogi
Author-email: kushagrarastogi2014@gmail.com
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
License-File: license.txt

# TOPSIS-Python

Submitted By: *Kushagra 101917112*

*

## What is TOPSIS

*Technique for **Order **Preference by **Similarity 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-Kushagra 101917112  can be run as in the following example:


### In Command Prompt

>> topsis data.csv "1,1,1,1" "+,+,-,+"


<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

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

<br>

## Output


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

<br>

