Topsis-Abhishek-102003364 0.2

This package can be used for implementation of Multiple criteria decision making using Topsis Algorithm. This is a python library for dealing with Multi-Criteria Decision Making (MCDM) problems by using techniques for order of preference by similarity to ideal solution (TOPSIS).

TOPSIS

Submitted By: Abhishek Gandhi 102003364

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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-abhishek-102003364

In Command Prompt

>> python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>
>> python 102003364.py 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 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	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**