Topsis-Navjot-102217046 1.0.3

Implementation of Topsis

TOPSIS Implementation

This repository contains a Python implementation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). TOPSIS is a powerful multi-criteria decision-making method that assists in ranking a set of alternatives based on their proximity to the ideal solution.

Table of Contents

1. Introduction
2. Usage
3. Command-line Arguments
4. Requirements

Introduction

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a well-established method for decision-making. This Python implementation allows you to easily apply TOPSIS to your decision matrix and obtain a ranked list of alternatives.

Key Concepts:

• Decision Matrix: Represents alternatives and criteria.
• Weights: Assign importance to criteria.
• Impacts: Indicate whether higher or lower values are favorable.
• Normalization: Ensures all criteria are on a similar scale.
• Ideal and Worst Solutions: Represent best and worst possible outcomes.
• Similarity and Dissimilarity Measures: Calculate proximity to ideal and dissimilarity to worst.
• TOPSIS Score: Combines similarity and dissimilarity measures.
• Ranking: Alternatives are ranked based on TOPSIS scores.

Usage

1. Ensure you have Python installed on your system.

2. Clone this repository to your local machine:

   git clone https://github.com/dhruvRajoria/Topsis_Dhruv
   

3. Navigate to the project directory:

   git clone https://github.com/dhruvRajoria/Topsis_Dhruv
   

4. Run the TOPSIS script with the required command-line arguments:

   python 102217050.py 102217050-data.csv "1,1,1,2" "+,+,-,+" result.csv

5. The TOPSIS analysis will be performed, and the result will be saved to the specified CSV file.

Command-line Arguments

• <InputDataFile>: Path to the input CSV file containing the decision matrix.

• <Weights>: Comma-separated weights for each criterion.

• <Impacts>: Comma-separated impact signs for each criterion (use '+' for beneficial criteria and '-' for non-beneficial criteria).

• <ResultFileName>: Desired name for the output CSV result file.

Requirements

• Python 3.x
• pandas
• numpy