topsis-gaurav-102303493 1.0.6

A Python package implementing the TOPSIS method for multi-criteria decision making.

Project Description

topsis-gaurav-102303493

topsis-gaurav-102303493 is a Python package for solving Multiple Criteria Decision Making (MCDM) problems using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

It helps rank alternatives based on their relative closeness to the ideal solution and is useful in real‑world decision‑making scenarios such as:

• Choosing the best product
• Selecting the best candidate
• Ranking investment options
• Engineering design evaluation
• Research and management decision analysis

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Installation

Use the package manager pip to install the package:

pip install topsis-gaurav-102303493

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Usage

Enter the CSV filename followed by the weights vector (comma‑separated) and the impacts vector (comma‑separated + or -).

Format

topsis input.csv "w1,w2,w3,..." "+,-,+,..." output.csv

Example

topsis sample.csv "1,1,1,1" "+,-,+,+" result.csv

Vectors can also be provided without quotes if they contain no spaces:

topsis sample.csv 1,1,1,1 +,-,+,+ result.csv

To view help information:

topsis -h

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Example Dataset

sample.csv

A CSV file showing data for different mobile handsets with varying features:

Model	Storage Space (GB)	Camera (MP)	Price ($)	Looks (out of 5)
M1	16	12	250	5
M2	16	8	200	3
M3	32	16	300	4
M4	32	8	275	4
M5	16	16	225	2

Weights vector:

[0.25, 0.25, 0.25, 0.25]

Impacts vector:

[+, +, -, +]

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Sample Command

topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" output.csv

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Sample Output

----------------------------
       TOPSIS RESULTS
----------------------------
Alternative   Score     Rank
1             0.534277  3
2             0.308368  5
3             0.691632  1
4             0.534737  2
5             0.401046  4

The output CSV file will contain the TOPSIS score and rank for each alternative.

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Important Notes

• The first column of the CSV file must contain the alternative names (e.g., M1, M2, ...).
• The remaining columns must contain numerical values only.
• The number of weights must match the number of criteria columns.
• The number of impacts must match the number of criteria columns.
• Impacts must be either + (benefit) or - (cost).
• Do not include categorical (non‑numeric) data in criteria columns.

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Author

Developed by Gaurav Srivastava

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License

This project is released for academic and educational use.