topsis-Tanisha-102203818 0.1.2

A Python package for implementing TOPSIS for multi-criteria decision making

TOPSIS-Tanisha-102203818

Effortlessly rank and evaluate alternatives using the TOPSIS method for multi-criteria decision-making.

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🌟 Introduction

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a decision-making approach designed to handle complex scenarios involving multiple criteria. With this package, users can seamlessly compute rankings based on their data, weights, and impact preferences.

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💡 Key Features

• Simplifies multi-criteria decision-making through an easy-to-use command-line interface.
• Fully customizable to user-defined weights and impacts.
• Provides accurate scores and ranks for alternatives based on the TOPSIS methodology.
• Supports flexible .csv input and output formats.

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🔧 Installation Guide

Install the package directly from PyPI: bash pip install topsis-Tanisha-102203818

Alternatively, clone the GitHub repository to use it locally: bash git clone https://github.com/tanisha1234-sys/topsis-Tanisha-102203818.git cd topsis-Tanisha-102203818 pip install -r requirements.txt

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🚀 How to Use

The package can be executed via the command line with the following syntax:

bash python <program.py>

Example Usage:

bash

python 102203818.py data.csv "1,2,3,4" "+,-,+,-" result.csv

Parameters:

• InputDataFile: Path to the input .csv file containing the data.
• Weights: Comma-separated numeric values representing the weights of each criterion.
• Impacts: Comma-separated values (+ or -) indicating whether the criterion is beneficial or non-beneficial.
• ResultFileName: Path to save the result as a .csv file.

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📝 Input Requirements

The input .csv file must:

1. Contain at least three columns:
   • The first column should list the alternatives (e.g., Option1, Option2).
   • The remaining columns should contain numeric values representing criteria.
2. Be properly formatted with no missing or non-numeric values in the criteria columns.

Sample Input (data.csv):

Object	Criterion 1	Criterion 2	Criterion 3	Criterion 4
A1	25	30	45	20
A2	35	25	50	15

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📊 Output File

The output .csv file includes all the columns from the input file with two additional columns:

• Topsis Score: The relative closeness to the ideal solution.
• Rank: The position of each alternative based on its TOPSIS score.

Example Output (result.csv):

Object	Criterion 1	Criterion 2	Criterion 3	Criterion 4	Topsis Score	Rank
A1	25	30	45	20	0.76	1
A2	35	25	50	15	0.65	2

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🛠 Error Handling

The program ensures:

• Correct input format and number of parameters.
• Validation of numeric criteria and proper weights/impacts format.
• Graceful handling of file-related errors (e.g., missing files).

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📦 Dependencies

Ensure the following libraries are installed:

• numpy
• pandas

Install them via: bash pip install numpy pandas

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📚 Additional Resources

• TOPSIS Explanation on Wikipedia
• Guide to Upload Python Packages to PyPI

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👤 About the Author

• Name: Tanisha
• Roll Number: 102203818
• Email: [e-mail]
• GitHub Profile: tanisha1234-sys

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📝 License

This project is licensed under the MIT License.