Topsis-ArnavAgarwal-102203985 1.0.0

A Python package for performing TOPSIS analysis.

TOPSIS Package

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision analysis method. This Python package implements TOPSIS, allowing users to rank alternatives based on weights and impacts of criteria.

Features

• Handles datasets with numerical attributes.
• Supports user-defined weights and impacts.
• Generates a complete ranking of alternatives.
• Simple and intuitive interface for implementation.

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Installation

Install the package using pip:

pip install Topsis-ArnavAgarwal-102203985

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How to Use

Input Format

The input dataset should be a CSV or Excel file containing numerical columns. The first column must uniquely identify the alternatives (e.g., names or IDs), and the remaining columns should represent the criteria.

Example data.csv:

Alternative	Criterion 1	Criterion 2	Criterion 3	Criterion 4
Alt1	250	16	12	5
Alt2	200	20	15	10
Alt3	300	12	10	8
Alt4	275	18	11	9

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Implementation

Step 1: Import the package

from topsis import topsis

Step 2: Define inputs

• Filepath: Path to the input file (CSV or Excel).
• Weights: List of weights assigned to each criterion (must sum to 1).
• Impacts: List indicating whether a criterion has a positive or negative impact (+ or -).

Example:

file_path = 'data.csv'
weights = [0.4, 0.3, 0.2, 0.1]
impacts = ['+', '+', '-', '+']

Step 3: Execute the TOPSIS method

result = topsis(file_path, weights, impacts)

Step 4: View results

print(result)
# Output: DataFrame with alternatives ranked based on their performance

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Example Use Case

Imagine you're choosing the best laptop based on criteria like price, performance, battery life, and weight. Here’s how to decide using TOPSIS:

Input Data

Laptop	Price (₹)	Performance (score)	Battery Life (hrs)	Weight (kg)
Laptop A	50000	8	5	1.5
Laptop B	60000	9	6	2.0
Laptop C	45000	7	4	1.3
Laptop D	55000	8	7	1.8

Implementation

from topsis import topsis

# Define inputs
file_path = 'laptops.csv'
weights = [0.3, 0.4, 0.2, 0.1]
impacts = ['-', '+', '+', '-']  # Lower price is better (-)

# Run TOPSIS
result = topsis(file_path, weights, impacts)

# Display the ranking
print(result)

Output

Laptop	Rank
Laptop D	1
Laptop B	2
Laptop A	3
Laptop C	4

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Use Cases

1. Business Decision-Making: Rank suppliers or partners based on multiple criteria.
2. Product Selection: Choose the best product based on features, cost, and performance.
3. Policy Analysis: Evaluate alternatives for policy implementation.
4. Academic Research: Use as a ranking tool in multi-criteria decision analysis.

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License

This package is licensed under the MIT License. See the LICENSE file for details.

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Contributing

Contributions are welcome! Feel free to open issues or submit pull requests for improvements or feature additions.

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Authors

• Your Name(Arnav Agarwal)
• Your GitHub Profile