semvec/pypi-packages huggingface dataset:
Topsis-Manik-102217009 0.1.0
A Python package implementing the TOPSIS method
Requires Python: >=3.6

# Topsis-Manik-102217009

**Topsis-Manik-102217009** is a Python package that implements the **TOPSIS** (Technique for Order of Preference by Similarity to Ideal Solution) method. This technique is commonly used for decision-making and multi-criteria decision analysis (MCDA) to rank and select alternatives based on various criteria.

## Features

- Implements the **TOPSIS** method for decision-making.
- Helps rank alternatives based on similarity to an ideal solution.
- Easy to use and integrate into any Python project.

## Installation

You can install the package directly from PyPI using pip:

```bash
pip install Topsis-Manik-102217009
```

## Usage

To use **Topsis-Manik-102217009**, you will need to prepare your data in either an Excel (`.xlsx`) or CSV (`.csv`) format. The first column should contain the names/labels of the alternatives, and the subsequent columns should contain the criteria values. The command line interface can be used as follows:

```bash
python 102217009.py <InputDataFile> <Weights> <Impacts> <ResultFileName>
```
topsis-102216116 1.0.2
A CLI tool for TOPSIS decision-making
Requires Python: >=3.7

# TOPSIS CLI Tool
<i>TOPSIS</i> (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision-making method that helps evaluate and rank options based on their relative closeness to an ideal solution. This CLI tool provides a simple and efficient way to perform TOPSIS analysis directly from the command line.

## Features
Supports weighted and impact-based decision-making.
Reads input data from a CSV file.
Outputs ranked results with scores in an easy-to-read CSV format.
Lightweight and user-friendly command-line interface.

## Installation
### Install the package using pip:
```
pip install topsis-cli
```
## Usage
Run the CLI tool with the following syntax:
```
topsis <input_file> <weights> <impacts> <output_file>
```
input_file: Path to the input CSV file containing decision matrix.

weights: Comma-separated list of weights for each criterion (e.g., 0.4,0.3,0.2,0.1).

impacts: Comma-separated list of impacts for each criterion, either + (positive) or - (negative) (e.g., +,+,-,+).

output_file: Path to save the output CSV file with scores and ranks.

### Example
Input File (data.csv):
```
Name	Criterion 1	Criterion 2	Criterion 3	Criterion 4
Option A	250	16	12	5
Option B	200	18	8	3
Option C	300	14	10	4
Option D	275	15	9	2
```
Command:
```
topsis data.csv 0.4,0.3,0.2,0.1 +,+,-,+ results.csv
```
Output File (results.csv):
```
Name	Criterion 1	Criterion 2	Criterion 3	Criterion 4	Score	Rank
Option A	250	16	12	5	0.75	2
Option B	200	18	8	3	0.50	4
Option C	300	14	10	4	0.80	1
Option D	275	15	9	2	0.65	3
```
### Requirements
Python 3.7 or higher <br>
Libraries: numpy, pandas <br>

### License
This project is licensed under the MIT License. See the LICENSE file for details.

### Contributing
Contributions are welcome! Feel free to open issues or submit pull requests.

## Author
<b>Naman Babbar<b>
topsis-cli 1.0.1
A CLI tool for TOPSIS decision-making
Requires Python: >=3.7

# TOPSIS CLI Tool
<i>TOPSIS</i> (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision-making method that helps evaluate and rank options based on their relative closeness to an ideal solution. This CLI tool provides a simple and efficient way to perform TOPSIS analysis directly from the command line.

## Features
Supports weighted and impact-based decision-making.
Reads input data from a CSV file.
Outputs ranked results with scores in an easy-to-read CSV format.
Lightweight and user-friendly command-line interface.

## Installation
### Install the package using pip:
```
pip install topsis-cli
```
## Usage
Run the CLI tool with the following syntax:
```
topsis <input_file> <weights> <impacts> <output_file>
```
input_file: Path to the input CSV file containing decision matrix.

weights: Comma-separated list of weights for each criterion (e.g., 0.4,0.3,0.2,0.1).

impacts: Comma-separated list of impacts for each criterion, either + (positive) or - (negative) (e.g., +,+,-,+).

output_file: Path to save the output CSV file with scores and ranks.

### Example
Input File (data.csv):
```
Name	Criterion 1	Criterion 2	Criterion 3	Criterion 4
Option A	250	16	12	5
Option B	200	18	8	3
Option C	300	14	10	4
Option D	275	15	9	2
```
Command:
```
topsis data.csv 0.4,0.3,0.2,0.1 +,+,-,+ results.csv
```
Output File (results.csv):
```
Name	Criterion 1	Criterion 2	Criterion 3	Criterion 4	Score	Rank
Option A	250	16	12	5	0.75	2
Option B	200	18	8	3	0.50	4
Option C	300	14	10	4	0.80	1
Option D	275	15	9	2	0.65	3
```
### Requirements
Python 3.7 or higher <br>
Libraries: numpy, pandas <br>

### License
This project is licensed under the MIT License. See the LICENSE file for details.

### Contributing
Contributions are welcome! Feel free to open issues or submit pull requests.

## Author
<b>Naman Babbar<b>
Topsis-Samarth-102203487 0.1
A package for TOPSIS method implementation.
Requires Python: >=3.6

# TOPSIS-Package

This Python package implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for multi-criteria decision-making.

## Usage
Install the package using:
```bash
pip install Topsis-Samarth-102203487