Topsis-Angad-Singh-Marwaha-102217055 0.1.0

A Python package for implementing the TOPSIS method.

TOPSIS Implementation in Python

Overview This Python script implements TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), a multi-criteria decision-making method to rank alternatives based on criteria weights and impacts.

Requirements

• Python 3.x
• Libraries: Install via
  pip install pandas numpy

Files

1. 102217055.py: Main Python script.
2. Input File: CSV containing alternatives and criteria.
3. Output File: CSV with calculated scores and ranks.

Input Format

• First Column: Alternative names (e.g., A, B, C).
• Other Columns: Numerical criteria values.

Example (data.csv): Alternatives,Criterion1,Criterion2,Criterion3 A,20,300,50 B,25,250,60 C,30,200,70

Usage Run the script with: python 102217055.py

Arguments:

1. : Path to input CSV.
2. : Comma-separated weights (e.g., 1,1,1).
3. : Comma-separated impacts (+ for beneficial, - for non-beneficial).
4. : Path to save output CSV.

Example: python 102217055.py data.csv "1,1,1" "+,+,-" result.csv

Output Format The output CSV includes the input data with:

• Topsis Score: Calculated score for each alternative.
• Rank: Based on TOPSIS score.

Example (result.csv): Alternatives,Criterion1,Criterion2,Criterion3,Topsis Score,Rank A,20,300,50,0.78,2 B,25,250,60,0.85,1 C,30,200,70,0.63,3

Error Handling

1. File Errors: Ensure the input file exists and is correctly formatted.
2. Mismatch in Weights/Impacts: Ensure their count matches the criteria columns.
3. Invalid Impacts: Use only + or -.

Method Overview

1. Normalize: Scale values for comparability.
2. Weighting: Apply weights to normalized values.
3. Ideal Values: Determine best and worst values for criteria.
4. Distance Calculation: Measure distances from ideal best/worst.
5. Score & Rank: Compute scores and rank alternatives.

This script simplifies multi-criteria decision-making. Reach out for support if needed!