Metadata-Version: 2.1
Name: topsis-aayushi-102103421
Version: 0.1.4
Summary: Topsis Implementation Package
Author: Aayushi Bareja
Author-email: aayushi.bareja@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE.text
Requires-Dist: pandas
Requires-Dist: numpy


## Overview

This is a Python package that provides an implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. TOPSIS is a multi-criteria decision-making method that helps in ranking a set of alternatives by evaluating them based on multiple criteria.

## Installation

You can install the package using pip:

pip install topsis-aayushi-102103421

## USAGE
from topsis-aayushi-102103421 import topsis

#Example data (replace this with your actual data)
data = {
    'Alternative1': [1, 2, 3, 4],
    'Alternative2': [4, 3, 2, 1],
    # Add more alternatives and their values
}

#Criteria weights (replace this with your actual weights)
weights = [0.25, 0.25, 0.25, 0.25]

#Criteria impacts ('+' or '-' for each criterion)
impacts = ['+', '+', '+', '-']

#Perform TOPSIS analysis
result = topsis(data, weights, impacts)

#Display the ranking
print("Ranking:", result)


## Parameters
data: A dictionary where keys are alternative names, and values are lists representing the performance values for each criterion.

weights: A list of weights corresponding to the importance of each criterion.

impacts: A list of impacts ('+' or '-') corresponding to the desired effect of each criterion.

## License
This package is distributed under the MIT License - see the LICENSE file for details.
