Metadata-Version: 2.1
Name: Topsis-Deepankar-Varma-102003431
Version: 0.16
Summary: This is a topsis package of Deepankar Varma version 0.16
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: _0.0.7_.

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 pip install topsis-Deepankar-102003431==0.0.1

### In Command Prompt

> > topsis data.csv "1,1,1,1,1" "+,+,-,+,-" 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    | 0.7 | 0.5 | 7   | 37  | 11.3 | 0.28016      | 5    |
| M2    | 0.8 | 0.6 | 7   | 46  | 13.4 | 0.8292       | 1    |
| M3    | 0.7 | 0.5 | 7   | 48  | 14   | 0.17536      | 8    |
| M4    | 0.9 | 0.8 | 7   | 44  | 13.2 | 0.25         | 7    |
| M5    | 0.9 | 0.9 | 5   | 37  | 11.1 | 0.56483      | 3    |
| M6    | 0.9 | 0.6 | 3   | 67  | 18   | 0.27313      | 6    |
| M7    | 0.9 | 0.5 | 7   | 39  | 11.8 | 0.55075      | 4    |
| M8    | 0.9 | 0.9 | 5   | 46  | 13.2 | 0.65029      | 2    |

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