topsis-decision-analysis 1.0.0

A Python implementation of the TOPSIS method for multi-criteria decision making

TOPSIS Implementation in Python

Course: UCS654 – Predictive Analytics using Statistics
Assignment: Assignment-1 (TOPSIS)
Author: Sartaj Singh Virdi
Roll Number: 102303259

About the Project

This project implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method using Python.

TOPSIS is a Multi-Criteria Decision Making (MCDM) technique that ranks multiple alternatives based on their relative closeness to an ideal best solution and an ideal worst solution. It is widely used in decision-making problems involving multiple conflicting criteria.

Features

• Command-line based TOPSIS tool
• Supports CSV and Excel input files
• Automatically computes:
  • Normalized decision matrix
  • Weighted normalized matrix
  • Ideal best & worst solutions
  • TOPSIS score
  • Final ranking of alternatives
• Easy to use and lightweight

Project Structure

topsis-mcdm-tool/
│── topsis/
│   ├── __init__.py
│   └── topsis.py
│── sample.csv
│── output.csv
│── setup.py
│── README.md

System Requirements

• Python: 3.7 or higher
• Libraries:
  • pandas
  • numpy

Installation

The package can be installed using pip after publishing to PyPI.

pip install topsis-mcdm-tool

(If installing locally for development)

pip install .

Usage

Run the following command in the terminal or command prompt:

topsis <inputFile> <weights> <impacts> <outputFile>

Parameters

• inputFile → CSV or Excel file containing data
• weights → Comma-separated weights for each criterion
• impacts → + for benefit, - for cost criteria
• outputFile → Output CSV/Excel file with scores and ranks

Example

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

Output

The output file will contain:

• Topsis Score
• Rank (lower rank = better alternative)

Sample Input Format

Model,Price,Performance,Camera,Battery
A,25000,8,7,4000
B,30000,9,8,4500
C,20000,7,6,3800

Output Columns

• Topsis Score – Closeness coefficient
• Rank – Ranking of alternatives

Conclusion

This project demonstrates the practical implementation of the TOPSIS algorithm for multi-criteria decision making using Python. It is suitable for academic use and real-world decision analysis problems.

License

This project is released under the MIT License.