Metadata-Version: 2.1
Name: Topsis-Parneet1-101917044
Version: 1.0.4
Summary: A package that calculates Topsis Score and Rank them accordingly
Home-page: https://github.com/Parneet-26/Topsis-python
Author: Parneet Kaur Rakhra
Author-email: prakhra_be19@thapar.edu
License: MIT
Description: Topsis-python
        TOPSIS
        Submitted By: Parneet Kaur Rakhra
        Title: Multiple Creteria Decision Making (MCDM) Using TOPSIS
        TOPSIS: Technique for Order of Preference By Similarity to Ideal Solution
        
        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.
        
        Algorithm used in the Program:
        Step 1:
        Check whether the arguments entered by users are sufficient as per the requirements of our package.
        Command should be like: topsis data_file.csv,"weights","impacts",result.csv
        
        Step2:
        Check whether weights and impact have same number of elements as that of number of columns in the csv file.
        
        Step3:
        Convert the column having categorical values to numerical values in the dataset.
        
        Step4:
        Vector normalisation is performed on the dataset and calculate the weighted normalised decision matrix..
        
        Step5:
        Calculate ideal best and ideal worst value in the dataset
        
        Step6:
        Calculate Euclidean distance from ideal best and ideal worst value.
        
        Step7:
        Finally calculate the Topsis Score and Rank
        
        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
