A Simple Package to detect and remove outliers using Inter-Quatile Range
Project 1 : UCS633
Submitted By: Manas Khatri 101703317
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. More details at wikipedia.
How to use this package:
TOPSIS-101703317 can be run as in the following example:
In Command Prompt
>> topsis data.csv "1,1,1,1" "+,+,-,+"
In Python IDLE:
>>> 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.
w) is not already normalised will be normalised later in the code.
Information of benefit positive(+) or negative(-) impact criteria should be provided in
Model Score Rank ----- -------- ---- 1 0.77221 2 2 0.225599 5 3 0.438897 4 4 0.523878 3 5 0.811389 1
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
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