Topsis-Aastha-102313059 1.0.1

TOPSIS implementation as a Python package

Topsis-Aastha-102313059

This Python package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for solving multi-criteria decision-making problems.
It ranks alternatives based on their performance across multiple criteria.

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Introduction

In many real-world problems, decisions depend on multiple factors such as cost, performance, risk, and return.
TOPSIS is a popular multi-criteria decision-making technique that identifies the best alternative by comparing distances from an ideal best and ideal worst solution.

The best alternative is:

• Closest to the positive ideal solution
• Farthest from the negative ideal solution

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TOPSIS Methodology

The TOPSIS approach consists of the following steps:

Step 1: Construct Decision Matrix

List alternatives and their performance under each criterion.

Step 2: Normalize the Matrix

rij = xij / √(∑xij²)

This removes scale differences among criteria.

Step 3: Weighted Normalized Matrix

vij = wj × rij

Step 4: Determine Ideal Solutions

Positive ideal solution = best values
Negative ideal solution = worst values

(based on benefit (+) or cost (−) criteria)

Step 5: Distance Calculation

Euclidean distance from ideal best and worst is calculated.

Step 6: TOPSIS Score

Score = Distance from worst / (Distance from best + Distance from worst)

Step 7: Ranking

Higher score indicates better alternative.

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Input Data Format

The Excel input file must contain:

Fund Name	P1	P2	P3	P4	P5
Fund A
Fund B

• First column contains alternatives
• Remaining columns contain numeric criteria values

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Result Table (Sample Output)

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.84	0.71	6.7	42.1	12.59	0.382109	6
M2	0.91	0.83	7.0	31.7	10.11	0.366492	7
M3	0.79	0.62	4.8	46.7	13.23	0.496361	4
M4	0.78	0.61	6.4	42.4	12.55	0.324792	8
M5	0.94	0.88	3.6	62.2	16.91	0.972128	1
M6	0.88	0.77	6.5	51.5	14.91	0.547048	3
M7	0.66	0.44	5.3	48.9	13.83	0.395015	5
M8	0.93	0.86	3.4	37.0	10.55	0.560092	2

The results show that Fund M5 achieved the highest TOPSIS score and is ranked as the best alternative.

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Result Visualization

The TOPSIS scores can be visualized using a bar graph to compare the performance of alternatives.

• X-axis: Fund Names
• Y-axis: TOPSIS Scores

This provides a clear comparison of rankings.

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Installation

Install the package using:

pip install Topsis-Aastha-102313059

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Usage

Run the package from command line:

topsis <InputFile> <Weights> <Impacts> <OutputFile>

### Example:

topsis data.xlsx "1,1,1,1,1" "+,+,-,+,+" result.csv

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## Requirements

- pandas  
- numpy  
- openpyxl  

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## Conclusion

This package offers a simple and efficient implementation of the TOPSIS decision-making technique and can be used for financial analysis, performance evaluation, and other multi-criteria decision problems.