topsis-anshul-102303930 1.0.2

A Python package for TOPSIS multi-criteria decision making

Topsis-Anshul-102303930

📌 Description

This package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, a multi-criteria decision-making (MCDM) approach used to rank alternatives based on their distance from an ideal best and an ideal worst solution.

TOPSIS is widely used in various domains including:

• Product selection and comparison
• Supplier evaluation
• Project prioritization
• Performance assessment
• Resource allocation

⚙️ Installation

Install the package using pip:

pip install Topsis-Anshul-102303930

🚀 Usage

After installation, the topsis command becomes available in your terminal.

Basic Syntax

topsis <input_csv> <weights> <impacts> <output_csv>

Parameters

Parameter	Description
input_csv	Path to the CSV file containing the decision matrix
weights	Comma-separated numerical weights for each criterion
impacts	Comma-separated impacts (+ for benefit, - for cost)
output_csv	Path where the output CSV file will be saved

Example Commands

With quotes:

topsis sample.csv "1,1,1,1" "+,-,+,+" output.csv

Without quotes:

topsis sample.csv 1,1,1,1 +,-,+,+ output.csv

📊 Example

Input File (sample.csv)

A CSV file showing data for different mobile handsets with varying features:

Model,Storage space(in gb),Camera(in MP),Price(in $),Looks(out of 5)
M1,16,12,250,5
M2,16,8,200,3
M3,32,16,300,4
M4,32,8,275,4
M5,16,16,225,2

Decision Criteria

Weights Vector: [0.25, 0.25, 0.25, 0.25]

Impacts Vector: [+, +, -, +]

• Storage space: + (more is better)
• Camera: + (more is better)
• Price: - (less is better)
• Looks: + (more is better)

Command

topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" output.csv

Output

The output file will contain the original data with two additional columns:

TOPSIS RESULTS
-----------------------------
Model  Storage space(in gb)  Camera(in MP)  Price(in $)  Looks(out of 5)  P-Score    Rank
M1     16                    12             250          5                0.534277   3
M2     16                    8              200          3                0.308368   5
M3     32                    16             300          4                0.691632   1
M4     32                    8              275          4                0.534737   2
M5     16                    16             225          2                0.401046   4

Interpretation: M3 ranks highest (Rank 1) with the best TOPSIS score of 0.691632, making it the optimal choice among the alternatives.

📋 Input File Requirements

1. CSV Format: The input file must be in CSV format
2. First Column: Should contain the names/identifiers of alternatives
3. Remaining Columns: Should contain numerical values for each criterion
4. No Missing Values: All cells must have valid numerical data (except the first column)
5. Minimum Criteria: At least 2 criteria columns are required

⚠️ Important Notes

• The number of weights must match the number of criteria columns
• The number of impacts must match the number of criteria columns
• Weights should be positive numbers
• Impacts should be either + (benefit) or - (cost)
• All criterion values must be numeric

🔧 How TOPSIS Works

1. Normalize the decision matrix
2. Apply weights to the normalized matrix
3. Identify ideal best and ideal worst solutions
4. Calculate the distance of each alternative from ideal best and ideal worst
5. Compute the performance score (closeness coefficient)
6. Rank alternatives based on performance scores

📝 License

This project is licensed under the MIT License.

👤 Author

Anshul
Roll Number: 102303930

🤝 Contributing

Contributions, issues, and feature requests are welcome!

📧 Contact

For any queries or suggestions, please feel free to reach out.

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Note: This package was created as part of an academic project for UCS654 - Prescriptive Analytics.