Topsis-Deepankar-Varma-102003431 0.19

This is a topsis package of Deepankar Varma version 0.19

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 [e-mail].

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

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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*