TOPSIS-Paras-101983048 1.0.3

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Metadata-Version:1.0 Name: TOPSIS-Paras-101983048 Version: 1.0.3 Summary: A Python package implementing TOPSIS technique. Home-page: UNKNOWN Author: Paras Author-email: [e-mail] License: MIT Platform: UNKNOWN Classifier: License :: OSI Approved :: MIT License Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.7 Description-Content-Type: text/markdown Requires-Dist: scipy Requires-Dist: tabulate Requires-Dist: numpy Requires-Dist: pandas Description: # TOPSIS-Python

Submitted By: Paras 101983048

pypi: <https://pypi.org/project/TOPSIS-Paras-101983048/1.0.3/> <br>

## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

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## How to use this package:

TOPSIS-Paras-101983048 can be run as in the following example:

### In Command Prompt ` >> pip install TOPSIS-Paras-101983048==1.0.3 ` >> python >>>from topsis_create.topsis_cal import topsis >>>topsis(“data.csv”,”1,1,1,2”,”+,+,-,+”)

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## Sample dataset

The decision matrix (a) 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 (w) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in I.

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## Output

` Model Score Rank ----- -------- ---- 1 0.639133 2 2 0.212592 5 3 0.407846 4 4 0.519153 3 5 0.828267 1 ` <br> The rankings are displayed in the form of a table using a package ‘tabulate’, with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.