TOPSIS_101803318

TOPSIS method for multiple-criteria decision making (MCDM)

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.

INSTALLATION

>> pip install TOPSIS_Yashwant_101803318

USAGE

>> 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 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**

License

By Yashwant

Contributors

Version Guidance

ReDoc Release	OpenAPI Specification
1.0.2	TOPSIS INIT