Metadata-Version: 2.1
Name: Topsis-Pramit-101903198
Version: 1.0.0.1
Summary: Multi-Criteria Decision Making
Home-page: https://github.com/Pramit29/Topsis-Pramit-101903198
Author: Pramit
License: MIT
Keywords: python,best model,Topsis,multi-criteria decision making,assignment
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas

# TOPSIS

Submitted By: **Pramit Deep Kaur - 101903198**.

Type: **Package**.

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

Version: **1.0.0.1**.

Date: **2022-02-26**.

Author: **Pramit Deep Kaur Gogna**.

Maintainer: **Pramit Deep Kaur Gogna <pramitdkgogna@gmail.com>**.

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.
It is a method where multiple models can be compared and scores can be assigned accordingly.

<br>

## How to install this package:

```
>> pip install Topsis-Pramit-101903198==1.0.2
```

### In Command Prompt

```
>> topsis 101903198-data.csv(your input data file) "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 | 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    |


<br>

## Output file (result.csv)

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank |
| ----- | ----------- | ------------- | ---- | -------- | ------------ | ---- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    | 0.7722       | 2    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    | 0.2255       | 5    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    | 0.4388       | 4    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    | 0.5238       | 3    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    | 0.8113       | 1    |

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