Topsis-Akshita-102203527 0.1.0

A Python package for implementing the TOPSIS method.

TOPSIS Implementation in Python

Introduction

This Python script implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for multi-criteria decision-making. It evaluates alternatives based on multiple criteria, considering their relative importance and impact.

Prerequisites Python 3.x

Libraries: pandas numpy

Install the required libraries using the following command: pip install pandas numpy

Files 102203527.py: The main Python script containing the TOPSIS implementation. Input File: A CSV file containing the decision matrix. Output File: A CSV file where the results will be saved.

Input File Format The input file must be a CSV file with the following format: First column: Names or identifiers of alternatives (e.g., A, B, C). Remaining columns: Criteria values (numerical).

Example Input File (data.csv):

Command-Line Usage The script requires four command-line arguments: python 102203527.py

Arguments: : Path to the input CSV file (e.g., data.csv). : Comma-separated weights for each criterion (e.g., 1,1,1,1). : Comma-separated impacts for each criterion (+ for beneficial, - for non-beneficial). : Path to the output CSV file (e.g., result.csv).

Example Command: python 102203527.py data.csv "1,1,1,1" "+,-,+,+" result.csv

Output File Format The output file will be a CSV file containing the input data with two additional columns: Topsis Score: The calculated score for each alternative. Rank: The rank of each alternative based on the TOPSIS score.

Example Output File Alternatives,Criterion1,Criterion2,Criterion3,Criterion4,Topsis Score,Rank A,20,300,50,0.5,0.78,2 B,25,250,60,0.7,0.85,1 C,30,200,70,0.4,0.63,3

Error Handling

Common Errors: Invalid Input File: Ensure the file exists and follows the correct format. Error: File '' not found.

Insufficient Columns: Ensure the file contains at least three columns (one for alternatives and two for criteria). Error: Input file must contain at least three columns. Mismatch in Weights or Impacts: Ensure the number of weights and impacts matches the number of criteria columns.

Error: Number of weights and impacts must match the number of criteria columns. Invalid Impacts: Ensure impacts are either + or -. Error: Impacts must be '+' or '-'.

Explanation of the TOPSIS Method

Input Normalization: Normalize the decision matrix using:

Weight Assignment: Multiply each normalized value by its respective weight.

Determine Ideal Best and Worst Values: For beneficial criteria (+), the ideal best is the maximum value, and the ideal worst is the minimum value. For non-beneficial criteria (-), the ideal best is the minimum value, and the ideal worst is the maximum value.

Calculate Distances: Compute the Euclidean distance from the ideal best and worst values.

Calculate TOPSIS Score: Compute the relative closeness to the ideal solution:

Ranking: Rank the alternatives based on their scores in descending order.