semvec/pypi-packages huggingface dataset:
TOPSIS-GAGANDEEPSINGH-101803390 0.0.4
This library gives function of save_topsis to give analysis...
Requires Python: >=3.6

# Topsis Package by Gagandeep Singh 101803390

This package gives the function save_topsis(input,weights,target,output) which takes four parameters:
    1. input : is the location of input file
    2. weight : this takes the input value of every parameter weights "0.1,0.2,0.3,0.4"
    3. target : this takes the +ve and -ve as input to take whether to maximize or minimize a parameter
    4. output : is the location of the csv file where u want to save it


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topsis-NamanJain-101803065 1.0.1
Decision Making using topsis (Python Package)

# TOPSIS-Python

By: **Naman Jain - 101803065**

***
pypi: <https://pypi.org/project/topsis-NamanJain-101803065/1.0.0/>
<br>
git: <https://github.com/namanjain323232/topsis>
***

## 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?
```
pip install topsis-NamanJain-101803065
```

### In Command Prompt
```
topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```
<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>

## 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>
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.