semvec/pypi-packages huggingface dataset:
Topsis-JatinGoyal-102003307 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

**Project 1 : UCS654**

Submitted By: **Jatin Goyal 102003307**

## 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-JatinGoyal-102003307 can be run as in the following example:

### In Command Prompt

```
>> topsis 102003307-data.csv "1,1,1,1,1" "+,+,-,+,+" 102003307-result.csv
```

## 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>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1
```

<br>
topsisnandinisetia 0.0.1
This is a Python Package implementing TOPSIS
License: MIT
Keywords: topsis,mcda,TIET


## Installation

Install the Package using the command -

```bash
   $ pip install topsis-nandini
```
    
```bash
    >>> import topsis-nandini as tp
    >>>df= [
       ['MobilePhone','RAM','Memory','DisplaySize','Battery']
        ['A',16,12,250,5]
        ['B',16,8,200,3]
        ['C',32,16,300,4]
        ['D',32,8,275,4]
        ['E',16,16,225,2]
        ]
    >>> w="1,1,1,1"
    >>> i="+,+,+,+"
    >>> tp.topsis(df,w,i)
```
You may use the Package via commandline as
```bash
   $ python [package name] [path of csv as string] [list of weights as string] [list of sign as string] [output csv filename as string]
```