semvec/pypi-packages huggingface dataset:
topsis-rishabh-102203060 1.0.3
A Python package to perform TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)
Requires Python: >=3.6

# topsis_rishabh_102203060

`topsis_rishabh_102203060` is a Python package that implements the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for multi-criteria decision analysis.

## Installation

You can install the package using pip:

```bash
pip install topsis_rishabh_102203060
```

## Usage

### Input File Format

The input CSV file should have the following structure:

- The first column should contain the names of the models.
- The subsequent columns should contain the criteria values for each alternative.
- The first row should contain the headers for each column.

Example:

| model | Criteria 1 | Criteria 2 | Criteria 3 |
|-------------|------------|------------|------------|
| m1        | 260        | 18         | 14         |
| m2        | 220        | 16         | 10          |
| m3        | 320        | 14         | 16         |

### Command-line Usage

After installation, you can use the `topsis` command in the terminal:

```bash
topsis <input_file> <weights> <impacts> <output_file>
```

- `<input_file>`: Path to the input CSV file.
- `<weights>`: Comma-separated string of weights for the criteria.
- `<impacts>`: Comma-separated string of '+' or '-' indicating the desirability of the criteria.
- `<output_file>`: Path to the output CSV file to save the results.

## Example

```bash
topsis data.csv "0.4,0.3,0.1" "+,-,+" results.csv
```

This command will process the `data.csv` file using the specified weights and impacts and output the results to `results.csv`.

## NOTE

- Ensure that the input file has at least three columns: one for alternatives and at least two for criteria.
- All criteria columns should contain numeric values.
- The number of weights and impacts must match the number of criteria.
- Impacts should only be '+' or '-'.

## License

This project is licensed under the MIT License. See the LICENSE file for details.
Topsis-Dhruv-102217050 1.0.3
Implementation of Topsis
Keywords: Topsis, Topsis-Dhruv-102217050, Dhruv, Topsis-Dhruv, 102217050

# TOPSIS Implementation

This repository contains a Python implementation of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). TOPSIS is a powerful multi-criteria decision-making method that assists in ranking a set of alternatives based on their proximity to the ideal solution.

## Table of Contents

1. [Introduction](#introduction)
2. [Usage](#usage)
3. [Command-line Arguments](#command-line-arguments)
6. [Requirements](#requirements)

## Introduction

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a well-established method for decision-making. This Python implementation allows you to easily apply TOPSIS to your decision matrix and obtain a ranked list of alternatives.

### Key Concepts:

- Decision Matrix: Represents alternatives and criteria.
- Weights: Assign importance to criteria.
- Impacts: Indicate whether higher or lower values are favorable.
- Normalization: Ensures all criteria are on a similar scale.
- Ideal and Worst Solutions: Represent best and worst possible outcomes.
- Similarity and Dissimilarity Measures: Calculate proximity to ideal and dissimilarity to worst.
- TOPSIS Score: Combines similarity and dissimilarity measures.
- Ranking: Alternatives are ranked based on TOPSIS scores.

## Usage

1. Ensure you have Python installed on your system.

2. Clone this repository to your local machine:

   ```bash
   git clone https://github.com/dhruvRajoria/Topsis_Dhruv

3. Navigate to the project directory:

      ```bash
   git clone https://github.com/dhruvRajoria/Topsis_Dhruv

   
4. Run the TOPSIS script with the required command-line arguments:

   
      python 102217050.py 102217050-data.csv "1,1,1,2" "+,+,-,+" result.csv



5. The TOPSIS analysis will be performed, and the result will be saved to the specified CSV file.

## Command-line Arguments

- `<InputDataFile>`: Path to the input CSV file containing the decision matrix.

- `<Weights>`: Comma-separated weights for each criterion.

- `<Impacts>`: Comma-separated impact signs for each criterion (use '+' for beneficial criteria and '-' for non-beneficial criteria).

- `<ResultFileName>`: Desired name for the output CSV result file.



## Requirements

- Python 3.x
- pandas
- numpy