TOPSIS

Submitted By: Amogh Mittal :- 101803115.

Type: Package.

Title: Using TOPSIS for multiple-criteria decision making.

Version: 0.0.1.

Date: 13-11-2020.

Author: Amogh Mittal.

Maintainer: Amogh Mittalamoghmittal16@gmail.com.

Description: Evaluation of alternate methods based on multiple criteria using TOPSIS..

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What is TOPSIS?

TOPSIS stands for Technique for Order Preference by Similarity to Ideal Solution. It was 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_Amogh

In Command Prompt

>> topsis data.csv "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	Correlation	R2	RMSE	Accuracy
M1	0.79	0.62	1.25	60.89
M2	0.66	0.44	2.89	63.07
M3	0.56	0.31	1.57	62.87
M4	0.82	0.67	2.68	70.19
M5	0.75	0.56	1.3	80.39

Weights (weights) is not yet normalised but it 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	Correlation	R2	RMSE	Accuracy	Topsis_score	Rank
M1	0.79	0.62	1.25	60.89	0.7722	2
M2	0.66	0.44	2.89	63.07	0.2255	5
M3	0.56	0.31	1.57	62.87	0.4388	4
M4	0.82	0.67	2.68	70.19	0.5238	3
M5	0.75	0.56	1.3	80.39	0.8113	1

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