Metadata-Version: 2.1
Name: TOPSIS-Bhawika-101803532
Version: 0.0.1
Summary: A python package to implement TOPSIS on a given dataset
Home-page: https://github.com/Bhawika16/TOPSIS-Bhawika-101803532
Author: BHAWIKA ARORA
Author-email: bbhawika_be18@thapar.edu
License: MIT
Description: # TOPSIS-Python
        
        Submitted By: **BHAWIKA ARORA 101803532**
        
        ***
        pypi: <https://pypi.org/project/TOPSIS-Bhawika-101803532>
        <br>
        git: <https://github.com/Bhawika16/TOPSIS-Bhawika-101803532>
        ***
        
        ## What is TOPSIS
        
        **T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal
        **S**olution (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).
        
        <br>
        
        ## How to use this package:
        
        TOPSIS-Bhawika-101803532  can be run as in the following example:
        
        
        
        ### In Command Prompt
        ```
        >> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
        ```
        <br>
        
        
        
        ## Sample dataset
        
        The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.
        
        Model | Correlation | R<sup>2</sup> | 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`.
        
        <br>
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
