topsis-sandeepkaur-102483081 1.0.0

TOPSIS-Python-Assignment (Part 2)

TOPSIS-Python

Project Overview

This project implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) algorithm. It is a multi-criteria decision analysis method used to rank a finite number of alternatives based on their distance from the "ideal best" and "ideal worst" solutions.

This package provides a command-line tool to perform the analysis on any CSV dataset containing numerical data.

Installation

To run this project, ensure you have Python installed along with the following libraries:

pip install pandas numpy matplotlib

Methodology

The algorithm follows these standard steps:

1. Data Validation: Checks for missing values and ensures all input columns are numeric.
2. Vector Normalization: Reduces all criteria to the same scale.
3. Weighting: Multiplies the normalized matrix by the user-assigned weights.
4. Ideal Best & Worst: Identifies the ideal best and ideal worst values for each criterion.
5. Euclidean Distance: Calculates the distance of each alternative from the best and worst solutions.
6. Performance Score: Calculates a score (0 to 1) and ranks the alternatives (Higher Score = Better Rank).

Usage

The script is designed to be run from the command line.

python topsis.py <InputDataFile> <Weights> <Impacts> <ResultFileName>

Parameters

• InputDataFile: Path to the input CSV file.
• Weights: Comma-separated numbers (e.g., "1,1,1,1").
• Impacts: Comma-separated signs ("+" or "-").
• ResultFileName: Name of the output CSV file.

Example Execution

Below is a demonstration of the tool using the sample data provided in this assignment.

1. Command Used

python topsis.py data.csv "1,1,1,1,1" "+,+,-,+,-" result.csv

2. Output Results

The program calculates the TOPSIS Score and Rank for each fund.

Fund Name	Topsis Score	Rank
M1	0.4435	6
M2	0.4851	5
M3	0.2834	7
M4	0.5362	3
M5	0.2281	8
M6	0.6973	1
M7	0.5891	2
M8	0.5342	4

License

This project is open-source and available for educational purposes.