Topsis-Karan-102317145 1.0.0

A command-line implementation of the TOPSIS method.

Topsis-Karan-102317145

This is a simple command-line implementation of the TOPSIS method in Python.

TOPSIS stands for Technique for Order Preference by Similarity to Ideal Solution.
It is a multi-criteria decision-making method used to rank alternatives based on multiple criteria.

This package allows you to:

• Provide a CSV file
• Provide weights for each criterion
• Provide impact (+ or -) for each criterion
• Automatically calculate TOPSIS score
• Get ranking as output in a CSV file

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What This Tool Does

1. Cleans the dataset
2. Handles numeric and ordinal data
3. Drops invalid or text-only columns
4. Normalizes the data
5. Applies weights
6. Calculates ideal best and worst
7. Computes TOPSIS score
8. Ranks alternatives
9. Saves result in output CSV

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Installation

You can install the package using pip:

pip install Topsis-Karan-102317145

Make sure you are using Python 3.8 or above.

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Input File Format

Your CSV file must:

• Have at least 3 columns
• First column = Name / Identifier
• Remaining columns = Criteria
• Criteria must be numeric or ordinal

Example:

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How To Use (Command Line)

After installation, run: topsis data.csv "1,1,1" "-,+,+” output.csv

Explanation:

• data.csv → Input file
• "1,1,1" → Weights for each criterion
• "- , + , +" → Impact for each criterion
  • + means benefit (higher is better)
  • - means cost (lower is better)
• output.csv → Output file name

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Output

The output file will look like:

Higher score means better rank.

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Important Notes

• Number of weights must match number of criteria.
• Number of impacts must match number of criteria.
• Impacts must be only + or -.
• Missing values are automatically handled.
• Text-only columns are ignored.

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Author

Karan
Roll No: 102317145

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About TOPSIS

TOPSIS works by:

1. Normalizing the data
2. Applying weights
3. Finding ideal best and worst values
4. Measuring distance from ideal solution
5. Ranking based on closeness

It is commonly used in decision-making problems like:

• Product comparison
• Supplier selection
• Performance evaluation
• Ranking alternatives

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Thank you for using this package!!