semvec/pypi-packages huggingface dataset:
TOPSIS-shobhit-101803372 1.1.5
This package can be used to calculate the topsis score of multiple component data and rank them accordingly
License: MIT

# topsis_pckg
It is a Python package to find out the best value among the different data using the mathematical calculations.
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install this package.

```bash
pip install TOPSIS_shobhit_101803372
```

## About
TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those

To get started quickly, just use the following:

```bash
from TOPSIS_shobhit_101803372.topsis import topsis
topsis('inputfilename','Weights','Impacts','Outputfilename')
```
make ensure the weights and impacts should be in "" 

eg: "1,1,1,1" and "+,-,+,-"

## Pre-requisite
The data should be enclosed in the csv file. There must be more than 2 columns


## Result
the output(outputfilename)  is saved in the project folder with extra 2 columns with topsis score and rank.

## License
[MIT](https://choosealicense.com/licenses/mit/)
TOPSIS-Akshit-101803487 1.0.1
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Akshit 101803487**

***

## 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-Akshit-101803487
```


### In Command Prompt
```
>> topsis data.csv "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

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 | 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**