semvec/pypi-packages huggingface dataset:
TOPSIS-kartikey-101803606 1.0.0
TOPSIS Score Calculator
License: MIT
Keywords: TOPSIS

## TOPSIS-kartikey-101803606

**Assignment - 6 : UCS538**
Submitted By: **KARTIKEY NIGAM 101803606**

***
<br>
## How to use this package:

The package TOPSIS-kartikey-101803606 can be installed as follows:
```
>> pip install TOPSIS-kartikey-101803606
```
To run on command line do as follows:
```
>> python
>>>from topsis_kartikey.topsis import topsis
>>>topsis("data.csv", "1,1,1,2", "+,+,-,+", "output.csv")
```
# Here, data.csv file represents the input file and output.csv represents output file
<br>

# Also, weights are given as "1,1,1,2" and impacts are given as "+,+,-,+"
<br>
## Sample Input

This is a sample data input:

<table><thead><tr><th>Model</th><th>Performance Score</th><th>Rank</th></tr></thead><tbody><tr><td>1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>

## Output of this sample input

The output that will be generated from the following input data will be:
```
   Model  Performance Score  Rank
0      1           0.639133     2
1      2           0.212592     5
2      3           0.407846     4
3      4           0.519153     3
4      5           0.828267     1
```


Here the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 5 is the worst solution.

* * *
TOPSIS-Arindam-101816003 0.0.1
Python package for implementing TOPSIS method.
License: MIT
Keywords: topsis,thapar,data science

# TOPSIS-Python

Submitted By: **Arindam Sharma**

## 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 data.csv "1,1,1,1" "+,+,-,+" output.csv
```

<br>

## Sample dataset

The decision matrix (`a`) should be provided with more than two columns and should be in .csv format.

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`) and impacts (either + or -)  should also be provided alogn with.

<br>

## Output

The output comes out in a csv file as the format given below :
```
Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis Score | Rank
------------ | ------------- | ------------ | ------------- | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89 | 0.639133 | 2
M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.212592 | 5
M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.407846 | 4
M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.519153 | 3
M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.828267 | 1

```