semvec/pypi-packages huggingface dataset:
topsis-navdeep-102303787 0.0.5
TOPSIS implementation using Python
License: MIT
Requires Python: >=3.7

# Topsis-Navdeep-102303787

## Overview
This package implements the **TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)**
method using Python.

TOPSIS is a multi-criteria decision-making technique used to rank alternatives based on their
distance from an ideal best and an ideal worst solution.

This package can be used directly from the **command line** after installation.

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## Package Name
TOPSIS-NAVDEEP-102303787

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## Installation
Install the package from PyPI using pip:
pip install TOPSIS-ANSHIKA-102303042

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## Usage (Command Line)
topsis <InputDataFile> <Weights> <Impacts> <OutputResultFile>

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### Example
topsis data.csv "1,1,1,2" "+,+,-,+" result.csv

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## 📥 Input File Format
- The input file must be in **CSV format**
- The **first column** should contain names of alternatives
- Remaining columns should contain **numeric values only**
- Minimum **3 columns** are required

### Sample Input
Fund Name,P1,P2,P3,P4
M1,0.67,0.45,6.5,42.6
M2,0.60,0.36,3.6,53.3
M3,0.82,0.67,3.8,63.1

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## 📤 Output File Format
The output file is a CSV file containing:
- **Topsis Score** – relative closeness to the ideal solution
- **Rank** – ranking based on TOPSIS score

Higher score indicates a better alternative.

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##  Methodology (Brief)
1. Read input data from CSV file
2. Normalize the decision matrix
3. Apply weights to the normalized matrix
4. Determine ideal best and ideal worst values
5. Calculate distance from ideal best and worst
6. Compute TOPSIS score
7. Rank alternatives based on the score

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##  Error Handling
The package checks for:
- Incorrect number of arguments
- File not found errors
- Non-numeric values in criteria columns
- Mismatch in number of weights and impacts
- Invalid impact values (only `+` or `-` allowed)

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##  Dependencies
- Python 3.x
- Pandas
- NumPy

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##  Conclusion
This package provides a simple and effective way to perform TOPSIS analysis using Python.
It is useful for decision-making problems involving multiple criteria.