Topsis-Kunal-102053007 0.2

This is a topsis package of version 0.2

Topsis_Kunal_102053007

TOPSIS

Submitted By: Kunal Madan - 102053007.

Type: Package.

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

Version: 1.0.0.

Date: 2022-01-22.

Author: Kunal Madan.

Maintainer: Kunal Madan [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-Kunal-102053007

In Command Prompt

topsis data.csv "1,1,1,1,1" "+,+,-,+,-" 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	0.66	0.44	4.9	32.9	9.73
M2	0.65	0.42	5	44.5	12.64
M3	0.61	0.37	3.3	41.1	11.35
M4	0.62	0.38	6.5	64.1	17.9
M5	0.83	0.69	6.4	34.4	10.58
M6	0.88	0.77	6.7	34.8	10.79
M7	0.63	0.4	4.5	39.1	11.16
M8	0.8	0.64	4.4	45.7	12.89

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 (out.csv)

Model	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.66	0.44	4.9	32.9	9.73	0.21189353869415917	6
M2	0.65	0.42	5	44.5	12.64	0.3271776939542866	5
M3	0.61	0.37	3.3	41.1	11.35	0.15153299377397803	8
M4	0.62	0.38	6.5	64.1	17.9	0.5850881941527206	1
M5	0.83	0.69	6.4	34.4	10.58	0.49092824102735394	3
M6	0.88	0.77	6.7	34.8	10.79	0.5427136027309007	2
M7	0.63	0.4	4.5	39.1	11.16	0.2008035122309426	7
M8	0.8	0.64	4.4	45.7	12.89	0.48154813555267983	4

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