Topsis-Atharva-102303372 1.0.2

TOPSIS implementation using Python

TOPSIS Package

Overview

This package implements the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a widely used Multi-Criteria Decision Making (MCDM) method. It ranks alternatives based on their distance from an ideal best and an ideal worst solution.

The package is designed to work directly from the command line and processes data provided in CSV format.

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Features

• Accepts CSV input files with multiple decision criteria
• Supports user-defined weights for each criterion
• Supports benefit (+) and cost (-) impacts
• Generates a ranked output file with TOPSIS scores
• Handles invalid inputs with clear error messages

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Installation

Install the package using pip:

pip install Topsis-Atharva-102303372

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Usage

Run the package from the command line using:

python -m topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Example

python -m topsis 102303372-input.csv "1,1,1,2,3" "+,+,-,+,+" 102303372-output.csv

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Parameters

• <InputDataFile> Path to the CSV file containing the decision matrix. The first column must contain alternative names, and the remaining columns must contain numeric criteria values.

• <Weights> Comma-separated weights corresponding to each criterion (example: 1,2,1,1)

• <Impacts> Comma-separated impacts for each criterion Use + for benefit criteria and - for cost criteria (example: +,+,-,+)

• <ResultFileName> Name of the output CSV file where results will be saved

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Sample Input File

The input CSV file should contain alternatives in the first column, followed by numerical values for each criterion.

Example: 102303372-input.csv

Model,Storage,Camera,Price,Looks
A1,64,48,32000,4
A2,128,64,45000,5
A3,64,32,28000,3
A4,256,108,60000,5
A5,128,48,40000,4

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Output

The output CSV file contains the original data along with the following additional columns:

• Topsis Score
• Rank

The alternative with Rank 1 is considered the best choice.

Example Output

Model,Storage,Camera,Price,Looks,Topsis Score,Rank
A2,128,64,45000,5,0.812,1
A5,128,48,40000,4,0.684,2
A1,64,48,32000,4,0.552,3
A4,256,108,60000,5,0.421,4
A3,64,32,28000,3,0.305,5

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Error Handling

The package checks for:

• Missing or invalid input files
• Non-numeric values in criteria columns
• Mismatch between number of criteria, weights, and impacts
• Invalid impact symbols (must be + or -)

Appropriate error messages are displayed for each case.

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Author

Atharva Pandey

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License

This project is intended for academic and educational use.