Metadata-Version: 2.1
Name: topsis-ppruthi-101883058
Version: 2.0.0
Summary: A python package to identify the best model out of different models using TOPSIS
Home-page: UNKNOWN
Author: Pritpal Singh Pruthi
Author-email: ppruthi_be17@thapar.edu
License: UNKNOWN
Description: # Ranking System Using Topsis
        
        **Project 1 : UCS633**
        
        
        Submitted By: **Pritpal Singh Pruthi 101883058**
        
        ***
        pypi: <https://pypi.org/project/topsis-ppruthi-101883058/>
        ***
        
        ## Installation
        
        Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Ranking system.
        
        ```bash
        pip install topsis-ppruthi-101883058
        ```
        
        <br>
        
        ## How to use this package:
        
        topsis-ppruthi-101883058 can be run as done below:
        
        
        
        ### In Command Prompt
        ```
        >> topsis data.csv "1,1,1,1" "+,+,-,+"
        ```
        <br>
        
        ### In Python IDLE:
        ```python
        >>> import pandas as pd
        >>> import topsis
        >>> data = pd.read_csv('data.csv').values
        >>> data = data[:,1:]
        >>> w = [1,1,1,1]
        >>> impacts = ["+" , "+" , "-" , "+" ]
        >>> topsis.topsis(data,w,impacts)
        ```
        
        <br>
        
        ## Sample dataset
        
        The decision matrix 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 list is not already normalised will be normalised later in the code.
        
        Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.
        
        <br>
        
        ## Output
        
        ```
        Model   Score    Rank
        -----  --------  ----
          1    0.77221     2
          2    0.225599    5
          3    0.438897    4
          4    0.523878    3
          5    0.811389    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.
        
        
        ## License
        [MIT](https://choosealicense.com/licenses/mit/)
        
        
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
