Topsis-Prabhsimar-102483078 1.0.5

A Python package for TOPSIS multi-criteria decision making

Topsis-Prabhsimar-102483078

📌 Description

This package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, a popular multi-criteria decision-making (MCDM) technique.

TOPSIS is used to rank alternatives based on their relative distance from:

• an ideal best solution
• an ideal worst solution

It helps decision-makers choose the best option among multiple alternatives.

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🧠 Applications of TOPSIS

TOPSIS is widely used in:

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

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⚙️ Installation

Install the package using pip:

pip install Topsis-Prabhsimar-102483078

🚀 Usage

After installation, the topsis command becomes available in the 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

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📊 Example

Input File (sample.csv)

Model,Storage(in GB),Camera(in MP),Price(in $),Rating
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 → Benefit (+)
• Camera → Benefit (+)
• Price → Cost (-)
• Rating → Benefit (+)

Command

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

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📈 Output

The output CSV file will contain two additional columns:

• Topsis Score (closeness coefficient)
• Rank

Sample Output

Model	Storage	Camera	Price	Rating	Topsis Score	Rank
M3	32	16	300	4	0.69	1
M4	32	8	275	4	0.53	2
M1	16	12	250	5	0.53	3
M5	16	16	225	2	0.40	4
M2	16	8	200	3	0.30	5

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📋 Input File Requirements

• CSV format only
• First column must contain alternative names
• Remaining columns must be numeric
• No missing values
• Minimum 2 criteria columns required

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⚠️ Important Notes

• Number of weights must equal number of criteria columns
• Number of impacts must equal number of criteria columns
• Weights must be positive numbers
• Impacts must be either + or -
• All criterion values must be numeric

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🔧 How TOPSIS Works

1. Normalize the decision matrix
2. Apply weights to normalized values
3. Determine ideal best and ideal worst solutions
4. Compute distances from ideal best and worst
5. Calculate closeness coefficient
6. Rank alternatives based on score

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📝 License

This project is licensed under the MIT License.

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👤 Author

Prabhsimar Singh
Roll Number: 102483078

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📚 Academic Note

This package was developed as part of an academic assignment for
UCS654 – Prescriptive Analytics