Metadata-Version: 2.1
Name: Topsis-Mritunjay-102003030
Version: 1.0.1
Summary: Topsis package for Multiple Criteria Decision Making problems(MCDM) problems
Home-page: https://github.com/mritunjay-07/Topsis-Mritunjay-102003030
Author: Mritunjay Dubey
Author-email: mdubey_be20@thapar.edu
License: MIT
Description: # TOPSIS SCORE CALCULATOR
        By: **MRITUNJAY-102003030**
        
        ### Title: Multiple Criteria Decision Making(MCDM) Using TOPSIS
        
        ## WHAT IS TOPSIS
        TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' and is a pretty straightforward MCDA method.
        This is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).
        
        #### HOW TO INSTALL THE TOPSIS PACKAGE
        ```buildoutcfg
        pip install Topsis-Mritunjay-102003030
        ```
        
        #### FOR CALCULATING THE TOPSIS SCORE
        ```buildoutcfg
        Topsis data.csv "0.25,0.25,0.25,0.25" "+,+,-,+" result.csv
        ```
        
        ##### Input File(Example:data.csv):
        Argument used to pass the path of the input file which conatins a dataset having different fields and to perform the topsis mathematical operations
        ##### Weights(Example:"0.25,0.25,0.25,0.25")
        The weights to assigned to the different parameters in the dataset should be passed in the argument.**It must be seperated by ','.**
        ##### Impacts(Example:"+,+,-,+"):
        The impacts are passed to consider which parameters have a positive impact on the decision and which one have the negative impact.**Only '+' and '-' values should be passed and should be seperated with ',' only**
        ##### Output File(Example:result.csv):
        This argument is used to pass the path of the result file where we want the rank and score to be stored.
        
        ## EXAMPLE
        
        #### data.csv
        
        A csv file showing data for different mobile handsets having varying features.
        
        | Model  | Storage space(in gb) | Camera(in MP)| Price(in $)  | Looks(out of 5) |
        | :----: |:--------------------:|:------------:|:------------:|:---------------:|
        | M1 | 16 | 12 | 250 | 5 |
        | M2 | 16 | 8  | 200 | 3 |
        | M3 | 32 | 16 | 300 | 4 |
        | M4 | 32 | 8  | 275 | 4 |
        | M5 | 16 | 16 | 225 | 2 |
        
        weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]
        
        impacts vector = [ + , + , - , + ]
        
        ### INPUT:
        
        ```python
        topsis data.csv "0.25,0.25,0.25,0.25" "+,+,-,+" result.csv
        ```
        
        ### OUTPUT:
        ```
              TOPSIS RESULTS
        -----------------------------
        
            P-Score  Rank
        1  0.534277     3
        2  0.308368     5
        3  0.691632     1
        4  0.534737     2
        5  0.401046     4
        
        ``` 
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
