semvec/pypi-packages huggingface dataset:
TOPSIS-Razik-101803178 1.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

## What is TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (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.


## How to use this package:



TOPSIS-Razik-101803178 can be run as in the following example:

### In command prompt:

```bash
>> TOPSIS data.csv "1,1,2,1" "+,-,+,-" result.csv
```




## Sample Dataset

```python



Model	Correlation	  R2	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

```

## Output

```python
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1

```
TOPSIS-Muskan-101803504 0.1
A python package to implement TOPSIS on a given dataset
License: MIT

# TOPSIS-Python

Submitted By: *Muskan Gupta 101803504*

*
pypi: <https://pypi.org/project/TOPSIS-Muskan-101803504>
<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-Muskan-101803504  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