Metadata-Version: 2.1
Name: Topsis_Aditya_102103546
Version: 1.0.8
Summary: This Python script implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) algorithm.
Home-page: https://github.com/BROWNcoder4946/Topsis_pypi_package
Author: Aditya Vashishta
Author-email: adityavashishta3911@gmail.com
License: MIT
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: numpy

# TOPSIS - Technique for Order Preference by Similarity to Ideal Solution

## Introduction

This Python script implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) algorithm. TOPSIS is a multi-criteria decision-making method that helps in ranking a set of alternatives based on their proximity to the ideal solution.

### Prerequisites

- Python 3
- pandas
- numpy

### Installation
```pip install Topsis_Aditya_102103546```

### Running the Script in Editor

Run the TOPSIS script in IDE using the following format:

```
from Topsis import __main__

__main__.TOPSIS(input_file,weights,impacts,output_file)
```

- `<input_file>`: Path to the CSV file containing the input data.
- `<weights>`: Weights for each criterion separated by commas.
- `<impacts>`: Impacts for each criterion, either '+' or '-'.
- `<output_file>`: Path to the CSV file where the result will be saved.

### Input Format

The input file should be a CSV file with the first column representing the alternatives and the following columns representing different criteria. Atleast 3 columns shall be present.

### Output

The script generates a CSV file with the TOPSIS scores and ranks for each alternative.

## License

(c) 2024 Aditya Vashishta

This project is licensed under the MIT License.
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