semvec/pypi-packages huggingface dataset:
Topsis-Sudhit-102017137 0.1
This is a topsis package of version 0.1

## Topsis_Sudhit_102017137

# TOPSIS

Submitted By: Sudhit Soni - 102017137.

Type: Package.

Title: *TOPSIS method for multiple-criteria decision making (MCDM)*.

Version: 1.0.0.

Date: 2022-01-22.

Author: Sudhit Soni.

Maintainer: *Sudhit Soni <ssoni_be20@thapar.edu>*.

Description: *Evaluation of alternatives based on multiple criteria using TOPSIS method.*.

---

## What is TOPSIS?

*Technique for **Order **Preference by **Similarity to **Ideal **S*olution
(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.

<br>

## How to install this package:


>> pip install Topsis-Sudhit-102017137


### In Command Prompt


>> topsis data.csv "1,1,1,1,1" "+,+,-,+,-" result.csv


## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Fund Name	P1	    P2	    P3	P4	    P5
M1	        0.63	0.4	    6.4	64.4	17.96
M2	        0.85	0.72	3.2	69.6	18.59
M3	        0.94	0.88	4	31	    9.21
M4	        0.68	0.46	5.6	32.4	9.79
M5	        0.69	0.48	5.5	38.5	11.29
M6	        0.7	    0.49	5.7	34.1	10.25
M7	        0.93	0.86	6.6	60.5	17.22
M8	        0.62	0.38	3.6	64.5	17.28


Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (out.csv)

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	        0.77	0.59	3.4	64.7	17.37	0.590504815	3
M2	        0.76	0.58	6.1	51.5	14.74	0.402338394	6
M3	        0.61	0.37	4.2	53.9	14.77	0.406862418	5
M4	        0.7	    0.49	7	34.3	10.62	0.320024918	8
M5	        0.94	0.88	4.4	63.5	17.43	0.699428496	2
M6	        0.93	0.86	6.5	57	    16.32	0.568238573	4
M7	        0.91	0.83	3.2	59.6	16.14	0.757501032	1
M8	        0.67	0.45	7	64.3	18.11	0.327902786	7


<br>
The output file contains columns of input file along with two additional columns having Topsis_scoreÂ andÂ Rank
TOPSIS-Sahil-Rohilla-102183056 1.0.4
A Topsis package that takes inputs as CSV and generates scores in results CSV!

**TOPSIS_102183056**

TOPSIS method for multiple-criteria decision making (MCDM)

**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.


## ðŸ”— Usage
topsis data.csv "1,1,1,1" "+,+,-,+" result.csv

## ðŸ”— Input File(data.csv)

Model	Correlation	R2	RMSE	Accuracy

M1	0.79	0.62	1.25	60.89

M2	0.66	0.44	2.89	63.07

M3	0.56	0.31	1.57	62.87

M4	0.82	0.67	2.68	70.19

M5	0.75	0.56	1.3	80.39

## ðŸ”— Output File(result.csv)

Model	Correlation	R2	RMSE	Accuracy	Topsis_score	Rank

M1	0.79	0.62	1.25	60.89	0.7722	2

M2	0.66	0.44	2.89	63.07	0.2255	5

M3	0.56	0.31	1.57	62.87	0.4388	4

M4	0.82	0.67	2.68	70.19	0.5238	3

M5	0.75	0.56	1.3	80.39	0.8113	1