Topsis-ParneetKaur-101917044 1.0.4

A package that calculates Topsis Score and Rank them accordingly

topsis-python Package Description : Python package for TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) ALGORITHM.

Motivation : This is a part of project - I made for UCS633 - Data analytics and visualization at TIET. @Author : Sourav Kumar @Roll no. : 101883068

Algorithm : STEP 1 : Create an evaluation matrix consisting of m alternatives and n criteria, with the intersection of each alternative and criteria. Apply any preprocessing if required.

STEP 2 : The matrix is then normalised using the norm.

STEP 3 : Calculate the weighted normalised decision matrix.

STEP 4 : Determine the worst alternative and the best alternative.

STEP 5 : Calculate the L2-distance between the target alternative i and the worst condition.

STEP 6 : Calculate the similarity to the worst condition.

STEP 7 : Rank the alternatives according to final performance scores.

Getting started Locally : Run On Terminal python -m topsis.topsis <filename.csv> ex. python python -m topsis.topsis topsis.csv 0.25,0.25,0.25,0.25 -,+,+,+

Run In IDLE from topsis import topsis t = topsis.topsis('filepath', [list of weights], [list of impacts]) t.topsis_main()

Run on Jupyter Open terminal (cmd) jupyter notebook Create a new python3 file. If file <filename.csv> doesn't exists, then make sure to upload the file to jupyter env. from topsis import topsis t = topsis.topsis('filepath', [list of weights], [list of impacts]) t.topsis_main()

topsis_main() has been specifically designed to inhibit leakeage of inbuilt functions. topsis_main(debug=True) use this to display all the intermediate matrices. Make sure that filename.csv is in same directory where package is installed. PAPER : Find the research paper at arxiv.

OUTPUT : Prints out overall ml models ranked and the best model / alternative.

output result on jupyter output result on idle output result on cmd

TESTING : The package has been extensively tested on various datasets consisting varied types of expected and unexpected input data and any preprocessing , if required has been taken care of.