Metadata-Version: 2.1
Name: Topsis-Deepankar-Varma-102003431
Version: 0.19
Summary: This is a topsis package of Deepankar Varma version 0.19
Author: Deepankar Varma
Author-email: satiwkdprhrit@gmail.com
Description-Content-Type: text/markdown
Requires-Dist: pandas

## Topsis_Deepankar_Varma_102003431

# TOPSIS

Submitted By: _Deepankar Varma-102003431_.

Type: _Package_.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: _0.19_.

Date: _2022-01-29_.

Author: _Deepankar Varma_.

Maintainer: **Deepankar Varma <satwikdpshrit@gmail.com>**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## What is TOPSIS?

*Technique for **Order **Preference by **Similarity to **Ideal \*\*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.

<br>

## How to install this package:

> > pip install Topsis-Deepankar-Varma-102003431==0.19

### In Command Prompt

> > topsis 102003431-data.csv "1,1,1,1,2" "-,+,+,-,+" 102003431-result.csv

## Input file (data.csv)

The decision matrix 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 | P1  | P2   | P3  | P4   | P5    |
| ----- | --- | ---- | --- | ---- | ----- |
| M1    | 0.7 | 0.71 | 6.7 | 42.1 | 12.59 |
| M2    | 0.8 | 0.83 | 7   | 31.7 | 10.11 |
| M3    | 0.7 | 0.62 | 4.8 | 46.7 | 13.23 |
| M4    | 0.9 | 0.61 | 6.4 | 42.4 | 12.55 |
| M5    | 0.9 | 0.88 | 3.6 | 62.2 | 16.91 |
| M6    | 0.9 | 0.77 | 6.5 | 51.5 | 14.91 |
| M7    | 0.9 | 0.44 | 5.3 | 48.9 | 13.83 |
| M8    | 0.9 | 0.86 | 3.4 | 37   | 10.55 |

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

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

<br>

## Output file (result.csv)

| Model | P1   | P2   | P3  | P4   | P5    | Topsis Score | Rank |
| ----- | ---- | ---- | --- | ---- | ----- | ------------ | ---- |
| M1    | 0.93 | 0.86 | 4.4 | 52.6 | 14.7  | 0.457283053  | 6    |
| M2    | 0.67 | 0.45 | 3.7 | 47.9 | 13.18 | 0.172274243  | 8    |
| M3    | 0.61 | 0.37 | 5.8 | 65   | 17.95 | 0.560480297  | 2    |
| M4    | 0.94 | 0.88 | 6   | 40.7 | 12.13 | 0.491036776  | 3    |
| M5    | 0.69 | 0.48 | 3.8 | 55.6 | 15.14 | 0.239375223  | 7    |
| M6    | 0.93 | 0.86 | 5.3 | 47.1 | 13.55 | 0.486632047  | 4    |
| M7    | 0.93 | 0.86 | 6.9 | 69.9 | 19.65 | 0.822186901  | 1    |
| M8    | 0.95 | 0.9  | 3.1 | 61.6 | 16.64 | 0.460139442  | 5    |

<br>
The output file contains columns of input file along with two additional columns having *Topsis_score* and *Rank*
