semvec/pypi-packages huggingface dataset:
TOPSIS-samridhi-101903776 0.0.2
A Python package in which TOPSIS technique is implemented.
License: MIT

Package name = TOPSIS-samridhi-101903776

# About the project

As the name suggests, this package implements TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) which is a multi-criteria decision analysis method (MCDA or MCDM), which is a technique based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS).

This package hence provides with the TOPSIS score according to which a model's relative rank with  respect to other models with same number of entries is also provided.


### Installation

Install the package using pip as follows :

```
>> pip install TOPSIS-samridhi-101903776
```

### How to run in command prompt

First go to the directory (using cd following by your directory command) where it has been installed (prefreably the directory will be in main python folder -> Libs -> site-packages -> yourpackagename) then run the following commands :
```
>> python 101903776.py <InputDataFile> <Weights> <Impacts> <ResultFileName>
```
Eg:
```
>> python 101903776.py data.csv “1,1,1,2” “+,+,-,+” result.csv
```
Topsis-Parneet1-101917044 1.0.4
A package that calculates Topsis Score and Rank them accordingly
License: MIT

Topsis-python
TOPSIS
Submitted By: Parneet Kaur Rakhra
Title: Multiple Creteria Decision Making (MCDM) Using TOPSIS
TOPSIS: Technique for Order of Preference By Similarity to Ideal Solution

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.

Algorithm used in the Program:
Step 1:
Check whether the arguments entered by users are sufficient as per the requirements of our package.
Command should be like: topsis data_file.csv,"weights","impacts",result.csv

Step2:
Check whether weights and impact have same number of elements as that of number of columns in the csv file.

Step3:
Convert the column having categorical values to numerical values in the dataset.

Step4:
Vector normalisation is performed on the dataset and calculate the weighted normalised decision matrix..

Step5:
Calculate ideal best and ideal worst value in the dataset

Step6:
Calculate Euclidean distance from ideal best and ideal worst value.

Step7:
Finally calculate the Topsis Score and Rank

The output file contains columns of input file along with two additional columns having Topsis_score and Rank