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This repository contains for TOPSIS application on a dataset and the package establishing on PyPI.

Home Page: https://pypi.org/project/Topsis-Deepankar-Varma-102003431/

Python 100.00%
data dataset pypi pypi-package python topsis topsis-algorithm topsis-methodology data-analysis data-science

topsis-deepankar-varma-102003431's Introduction

Topsis_Deepankar_Varma_102003431

TOPSIS

Submitted By: Deepankar Varma-102003431.

Type: Package.

Title: TOPSIS method for multiple-criteria decision making (MCDM).

Version: 0.19.

Date: 2022-01-29.

Author: Deepankar Varma.

Maintainer: Deepankar Varma [email protected].

Description: Evaluation of alternatives based on multiple criteria using TOPSIS method..


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.


How to install this package:

pip install Topsis-Deepankar-Varma-102003431==0.19

In Command Prompt

topsis 102003431-data.csv "1,1,1,1,2" "-,+,+,-,+" 102003431-result.csv

Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.

Model P1 P2 P3 P4 P5
M1 0.7 0.71 6.7 42.1 12.59
M2 0.8 0.83 7 31.7 10.11
M3 0.7 0.62 4.8 46.7 13.23
M4 0.9 0.61 6.4 42.4 12.55
M5 0.9 0.88 3.6 62.2 16.91
M6 0.9 0.77 6.5 51.5 14.91
M7 0.9 0.44 5.3 48.9 13.83
M8 0.9 0.86 3.4 37 10.55

Weights (weights) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in impacts.


Output file (result.csv)

Model P1 P2 P3 P4 P5 Topsis Score Rank
M1 0.93 0.86 4.4 52.6 14.7 0.457283053 6
M2 0.67 0.45 3.7 47.9 13.18 0.172274243 8
M3 0.61 0.37 5.8 65 17.95 0.560480297 2
M4 0.94 0.88 6 40.7 12.13 0.491036776 3
M5 0.69 0.48 3.8 55.6 15.14 0.239375223 7
M6 0.93 0.86 5.3 47.1 13.55 0.486632047 4
M7 0.93 0.86 6.9 69.9 19.65 0.822186901 1
M8 0.95 0.9 3.1 61.6 16.64 0.460139442 5

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

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