semvec/pypi-packages huggingface dataset:
TOPSIS-Vaibhav-101803499 1.0.1
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Vaibhav 101803499**

***

## 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-Vaibhav-101803499
```


### 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**
Tahil 1.0.0
TOPSIS
License: MIT

# TOPSIS-Tahil-101803246
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.

## Installation
'''pip install TOPSIS-Tahil-101803246==1.0.0'''

## License
Â©  2020 Tahil
This repository is licensed under the MIT license. 
See LICENSE for details
TOPSIS-kashish-101803022 0.0.2
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

Submitted By: *Kashish  101803022*

*
pypi: <https://pypi.org/project/TOPSIS-kashish-101803022>
<br>

*

## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-kashish-1018035022 can be run as in the following example:



### In Command Prompt

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

<br>

### Using it in python ide
TOPSIS was programmed with ease-of-use in mind. Just, import topsis from TOPSIS-Muskan-101803504

    from Topsis_Muskan import Topsis

<br>

## Sample dataset

The decision matrix (`a`) 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 (`w`) is not already normalised will be normalised later in the code.

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

<br>

The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method
TOPSIS-Kartik-101803696 1.0.1
A Python package to get best out of various features using TOPSIS.
License: MIT

//setup.py 
//README.md
//LICENSE
//MANIFEST.in

# topsis_pckg
It is a Python package to find out the best value among the different data using the mathematical calculations.
A Python package to get best out of various features using TOPSIS.
## Installation

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

```bash
pip install TOPSIS-Kartik-101803696
```

## 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 topis_pckg.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.