Topsis-Saksham-102003673 0.0.7

A package -> Calculates Topsis Score and Ranks them accordingly

Topsis_Saksham Yadav - 102003673

TOPSIS

Submitted By: Saksham Yadav - 102003673.

Type: Package.

Title: TOPSIS method for multiple-criteria decision making (MCDM).

Version: 0.0.7.

Date: 2023-01-22.

Author: Saksham Yadav.

Maintainer: Saksham Yadav [e-mail].

Description: Evaluation of alternatives based on multiple criteria using TOPSIS method..

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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.

How to install this package:

>> pip install Topsis_Saksham_102003673

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, R², Root Mean Squared Error, Correlation, and many more.

| 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 |

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.

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 |

The output file contains columns of input file along with two additional columns having Topsis_score and Rank