Metadata-Version: 2.1
Name: Topsis-102003592-Bhavna
Version: 0.5
Summary: Evaluation of alternatives based on multiple criteria using TOPSIS method.
Home-page: UNKNOWN
Author: Bhavna Goyal
Author-email: bgoyal_be20@thapar.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: pandas

## Topsis_102003592_Bhavna

# TOPSIS - Multiple Criteria Decision Making

Submitted By: **Bhavna Goyal - 102003592**.

Type: **Package**.

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

Version: **0.5**.

Date: **21-01-2023**.

Author: **Bhavna Goyal**.

Maintainer: **Bhavna Goyal <bgoyal_be20@thapar.edu>**.

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

---

## 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.

<br>

## How to install this package:

```
>> pip install Topsis_102003592_Bhavna
```

### In Command Prompt

```
>> topsisbhav data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Input file (data.csv)

| Attribute | Price or cost | Storage Space | Camera| Looks |
| -----     | -----------   | ------------- | ---- | -------|
| Mobile 1  |250$           | 16GB          | 12MP | 5      |
| Mobile 2  | 200$          | 16GB          | 8MP  | 3      |
| Mobile 3  | 300$          | 32GB          | 16MP | 4      |
| Mobile 4  |  275$         | 32GB          | 8MP  | 4      |
| Mobile 5  | 225$          | 16GB          | 16MP | 2      |

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)

| Attribute | Price or cost | Storage Space | Camera| Looks | Topsis_score | Rank|
| -----     | -----------   | ------------- | ---- | -------| ------------ | ----|
| Mobile 1  |250$           | 16GB          | 12MP | 5      | 0.4228       | 4   |
| Mobile 2  |200$           | 16GB          | 8MP  | 3      | 0.4635       | 3   |
| Mobile 3  |300$           | 32GB          | 16MP | 4      | 0.5097       | 2   |
| Mobile 4  |275$           | 32GB          | 8MP  | 4      | 0.3772       | 5   |
| Mobile 5  |225$           | 16GB          | 16MP | 2      | 0.6871       | 1   |


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

