Metadata-Version: 2.1
Name: Topsis_IshaanTakkar_101803025
Version: 1.0.7
Summary: A package -> Calculates Topsis Score and Rank them accordingly
Home-page: https://github.com/takkar99/Topsis_IshaanTakkar_101803025
Author: Ishaan Takkar
Author-email: ishaantakkar@gmail.com
License: MIT
Description: 
        # TOPSIS
        
        Submitted By: **Ishaan Takkar - 101803025**.
        
        Type: **Package**.
        
        Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.
        
        Version: **1.0.3**.
        
        Date: **2020-11-13**.
        
        Author: **Ishaan Takkar**.
        
        Maintainer: **Ishaan Takkar <ishaantakkar@gmail.com>**.
        
        Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.
        
        ---
        
        ## 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.
        
        <br>
        
        ## How to install this package:
        
        ```
        >> pip install Topsis_IshaanTakkar_101803025
        ```
        
        ### In Command Prompt
        
        ```
        >> 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, 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 (`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`.
        
        <br>
        
        ## Output file (result.csv)
        
        | Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank |
        | ----- | ----------- | ------------- | ---- | -------- | ------------ | ---- |
        | M1    | 0.79        | 0.62          | 1.25 | 60.89    | 0.77         | 2    |
        | M2    | 0.66        | 0.44          | 2.89 | 63.07    | 0.23         | 5    |
        | M3    | 0.56        | 0.31          | 1.57 | 62.87    | 0.44         | 4    |
        | M4    | 0.82        | 0.67          | 2.68 | 70.19    | 0.52         | 3    |
        | M5    | 0.75        | 0.56          | 1.3  | 80.39    | 0.81         | 1    |
        
        <br>
        The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
