topsis-core 1.0.0

A command-line tool implementing the TOPSIS multi-criteria decision-making algorithm.

TOPSIS CLI

A lightweight command-line tool implementing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The tool accepts CSV or Excel datasets, applies user-defined weights and impacts, and generates a ranked output using the TOPSIS decision-making algorithm.

🌐 Live Demo

Try the web version here:

https://topsis-zzre.vercel.app/

📦 Installation

Install the package from PyPI:

pip install topsis-cli

🚀 Usage

Run the tool from the command line using:

topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Parameters

Parameter	Description
InputDataFile	Path to the input .csv or .xlsx file
Weights	Comma-separated numeric weights (e.g. 1,1,1,1)
Impacts	Comma-separated impacts using + (benefit) or - (cost)
ResultFileName	Output CSV filename

Input Requirements

• The dataset must contain at least three columns.
• The first column should contain the names of alternatives (e.g., M1, M2, M3).
• All remaining columns must contain numeric values.

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Example

Input (data.csv)

Model	Price	Storage	Camera	Looks
M1	250	16	12	5
M2	200	16	8	3
M3	300	32	16	4
M4	275	32	8	4
M5	225	16	16	2

Weights

1,1,1,1

Impacts

-,+,+,+

• Price → Cost (-)
• Storage → Benefit (+)
• Camera → Benefit (+)
• Looks → Benefit (+)

Run

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

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Output

The generated result.csv contains two additional columns:

• TOPSIS Score
• Rank

Model	Price	Storage	Camera	Looks	TOPSIS Score	Rank
M1	250	16	12	5	0.534277	3
M2	200	16	8	3	0.308368	5
M3	300	32	16	4	0.691632	1
M4	275	32	8	4	0.534737	2
M5	225	16	16	2	0.492650	4

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Features

• Supports CSV and Excel datasets
• Implements the complete TOPSIS algorithm
• User-defined weights and impacts
• Automatic ranking based on TOPSIS score
• Simple command-line interface
• Fast and lightweight implementation

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Project Links

• GitHub Repository: https://github.com/HarshitCodes16/topsis-cli
• Live Demo: https://topsis-zzre.vercel.app/

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License

This project is licensed under the MIT License.