Topsis-Kashish-102117150 1.0

A package for performing TOPSIS analysis

Topsis Implementation

This package is used for topsis implementation, published by Kashish, roll number- 102117150, group- 3CS6.

Overview

This Python package implements the Topsis (Technique for Order Preference by Similarity to Ideal Solution) algorithm. Topsis is a multi-criteria decision-making (MCDM) method that helps in choosing the best alternative from a set of alternatives based on their performance on multiple criteria.

Installation

To install the package, use the following command:

pip install topsis-Kashish-102117150

Data

Fund Name,P1,P2,P3,P4,P5

M1,0.79,0.62,4.8,66.6,18.2

M2,0.69,0.48,5,46.9,13.27

M3,0.77,0.59,3.3,43.9,12.14

M4,0.82,0.67,3.2,67,17.92

M5,0.65,0.42,5.9,34.9,10.47

M6,0.8,0.64,4.8,66.8,18.26

M7,0.75,0.56,3.9,31.7,9.23

M8,0.83,0.69,4.1,55.9,15.38

Usage

In the terminal,

python 102117150.py 102117150-data.csv "1,1,1,1,1" "+,-,+,-,+" 102117150-result.csv

where,

input_data.csv: The input CSV file containing the decision matrix. You can use the file 102117150-data.csv .

"1,1,1,1,1": Comma-separated weights for each criterion.

"+,-,+,-,+": Comma-separated impacts for each criterion (either '+' or '-').

output_result.csv: The desired name for the output CSV file containing Topsis scores and ranks. You can see the results in file- 102117150-result-1.csv for the respective weights and impacts.

Result

Fund Name,P1,P2,P3,P4,P5,Topsis Score,Rank

M1,0.79,0.62,4.8,66.6,18.2,0.7879309024122741,6

M2,0.69,0.48,5.0,46.9,13.27,0.4602057102586321,1

M3,0.77,0.59,3.3,43.9,12.14,0.3424774958156483,8

M4,0.82,0.67,3.2,67.0,17.92,0.6236353942759449,4

M5,0.65,0.42,5.9,34.9,10.47,0.3924844873537729,2

M6,0.8,0.64,4.8,66.8,18.26,0.7998958662123036,5

M7,0.75,0.56,3.9,31.7,9.23,0.23219941041702397,3

M8,0.83,0.69,4.1,55.9,15.38,0.6259888691115867,7

pip install topsis-Kashish-102117150