TOPSIS-YourName-RollNumber

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) - A Python package for multi-criteria decision analysis.

📋 Description

TOPSIS is a multi-criteria decision-making method that helps rank alternatives based on their similarity to the ideal solution. This package provides an easy-to-use implementation of the TOPSIS algorithm with comprehensive error handling and validation.

🎯 Features

• ✅ Complete TOPSIS algorithm implementation
• ✅ Command-line interface
• ✅ Comprehensive input validation
• ✅ Detailed error messages
• ✅ CSV input/output support
• ✅ Easy to use and integrate

📦 Installation

From PyPI

pip install Topsis-YourName-RollNumber

From Source

git clone https://github.com/yourusername/Topsis-YourName-RollNumber.git
cd Topsis-YourName-RollNumber
pip install -e .

🚀 Usage

Command Line

python -m Topsis-YourName-RollNumber <InputFile> <Weights> <Impacts> <OutputFile>

Example

python -m Topsis-YourName-RollNumber data.xlsx "1,1,1,2" "+,+,-,+" output.xlsx

As a Python Module

from Topsis_YourName_RollNumber import topsis_analysis

# Perform TOPSIS analysis
result_df = topsis_analysis(
    input_file='data.xlsx',
    weights='1,1,1,2',
    impacts='+,+,-,+',
    output_file='output.xlsx'
)

print(result_df)

📊 Input Format

Excel File Requirements (.xlsx)

1. Minimum 3 columns: First column for alternatives (names), remaining columns for numeric criteria
2. First column: Alternative names (non-numeric)
3. Remaining columns: Numeric values only

Example Input File (data.xlsx)

Fund Name	P1	P2	P3	P4	P5
M1	0.84	0.71	6.7	42.1	12.59
M2	0.91	0.83	7.0	31.7	10.11
M3	0.79	0.62	4.8	46.7	13.23
M4	0.78	0.61	6.4	42.4	12.55
M5	0.94	0.88	3.6	62.2	16.91

Parameters

• Weights: Comma-separated numeric values (e.g., "1,1,1,2")
• Impacts: Comma-separated + or - signs (e.g., "+,+,-,+")
  • + : Benefit criterion (higher is better)
  • - : Cost criterion (lower is better)

📤 Output Format

The output Excel file contains all input columns plus:

• Topsis Score: Performance score (0-1, higher is better)
• Rank: Ranking (1 = best alternative)

Example Output

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M5	0.94	0.88	3.6	62.2	16.91	0.6890	1
M8	0.93	0.87	5.5	53.3	15.07	0.6123	2
...	...	...	...	...	...	...	...

⚠️ Error Handling

The package validates:

• ✅ Correct number of arguments
• ✅ File existence
• ✅ Excel format (.xlsx)
• ✅ Minimum 3 columns
• ✅ Numeric values in criteria columns
• ✅ Matching number of weights and criteria
• ✅ Matching number of impacts and criteria
• ✅ Valid impact signs (+ or -)
• ✅ Proper comma separation

🧮 TOPSIS Algorithm Steps

1. Normalize the decision matrix
2. Apply weights to normalized matrix
3. Identify ideal best and ideal worst solutions
4. Calculate Euclidean distances from ideal solutions
5. Compute TOPSIS score
6. Rank alternatives

📚 Mathematical Formula

TOPSIS Score:

$$P_i = \frac{S_i^-}{S_i^+ + S_i^-}$$

Where:

• $S_i^+$ = Distance from ideal best
• $S_i^-$ = Distance from ideal worst

🛠️ Requirements

• Python >= 3.7
• pandas >= 1.0.0
• numpy >= 1.18.0
• openpyxl >= 3.0.0

📄 License

MIT License

👤 Author

Your Name

• Roll Number: Your Roll Number
• Email: your.email@example.com
• GitHub: @yourusername

🤝 Contributing

Contributions, issues, and feature requests are welcome!

📝 Changelog

Version 1.0.0 (Initial Release)

• Complete TOPSIS implementation
• CLI interface
• Comprehensive validation
• PyPI package

---

Made with ❤️ for Multi-Criteria Decision Making