topsis-saanvi-102483080 1.0.0

TOPSIS implementation for multi-criteria decision making

Project Description

Submitted by: Saanvi Wadhwa Roll No: 102483080 Group: 3C15

Overview

topsis-saanvi-102483080 is a Python package designed to solve multi-criteria decision-making (MCDM) problems using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The package allows users to evaluate multiple alternatives based on several criteria and ranks them according to their relative closeness to the ideal solution.

Key Features

Command-line based execution Supports any number of criteria and alternatives Accepts weights and impacts as comma-separated inputs Handles both benefit (+) and cost (-) criteria Produces a ranked output in CSV format

Installation

The package can be installed using pip. From TestPyPI: pip install -i https://test.pypi.org/simple/ topsis-saanvi-102483080 --extra-index-url https://pypi.org/simple

How to Use

Provide the input CSV file followed by the weights and impacts. Syntax: topsis <input.csv> <output.csv>

Example: topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" result.csv

Note: If weights or impacts contain spaces, enclose them within double quotes.

Sample Input

Example Dataset data.csv

The following dataset represents different investment funds evaluated across multiple parameters.

Fund Name	P1	P2	P3	P4	P5
M1	0.84	0.71	6.7	42.1	12.59
M2	0.91	0.83	7.0	31.7	10.11
M3	0.79	0.62	4.8	46.7	13.23
M4	0.78	0.61	6.4	42.4	12.55
M5	0.94	0.88	3.6	62.2	16.91
M6	0.88	0.77	6.5	51.5	14.91
M7	0.66	0.44	5.3	48.9	13.83
M8	0.93	0.86	3.4	37.0	10.55

Output

The output file includes: TOPSIS Score – numerical measure of preference Rank – relative ranking of alternatives Higher score indicates a better alternative.

Assumptions

The first column contains alternative names. Remaining columns must be numeric. Number of weights must match number of criteria. Impacts can only be + or -.

License

This project is released under the MIT License and is intended for academic use.

Author

Saanvi Wadhwa