Project 1 : UCS633

Submitted By: Khushnuma Grover 101703289

pypi: https://pypi.org/project/topsis-101703289-khushnuma
git: https://github.com/khushgrover/topsis-python.git

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. More details at wikipedia.

TOPSIS-101703289-KHUSHNUMA can be run as in the following example:

>> topsis data.csv "1,1,1,1" "+,+,-,+"

>>> import pandas as pd
>>> from topsis_python.topsis import topsis
>>> dataset = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>> topsis(d,w,im)

The decision matrix (a) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R², Root Mean Squared Error, Correlation, and many more.

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.

Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1