semvec/pypi-packages huggingface dataset:
topsis-gurkirat-102303256 1.1.1
A professional command-line tool for TOPSIS decision analysis.

# TOPSIS Implementation

**BY**: Gurkirat Singh  
**Roll Number**: 102303256  
**Group**: 3C21  

`topsis-gurkirat-102303256` is a Python library for solving **Multiple Criteria Decision Making (MCDM)** problems using the **Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)**.

---

##  Installation

Install the package from PyPI using pip:

```bash
pip install topsis-gurkirat-102303256
```

---

##  Usage

Run the package via the command line:

```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

### Example

```bash
topsis topsis_dataset.csv "0.2,0.15,0.15,0.2,0.15,0.15" "-,+,+,+,+,+" result.csv
```

---

##  Steps Followed in TOPSIS

1. **Normalization**  
   Transform the data into a comparable scale.

2. **Weighting**  
   Apply user-defined weights to the normalized decision matrix.

3. **Ideal Solutions**  
   Determine the ideal best and ideal worst values for each criterion.

4. **Distance Calculation**  
   Compute the Euclidean distance of each alternative from the ideal best and ideal worst.

5. **Closeness Score**  
   Calculate the relative closeness of each alternative to the ideal solution.

6. **Ranking**  
   Rank the alternatives based on the TOPSIS score.

---

##  Sample Dataset

| Alternative | Cost | Quality | Delivery Time | Durability | Service | Energy Efficiency |
|------------|------|---------|---------------|------------|---------|-------------------|
| A1 | 250 | 7 | 5 | 8 | 8 | 6 |
| A2 | 200 | 6 | 7 | 7 | 7 | 7 |
| A3 | 300 | 8 | 4 | 9 | 9 | 8 |
| A4 | 275 | 7 | 6 | 8 | 8 | 7 |
| A5 | 225 | 9 | 3 | 9 | 8 | 9 |
| A6 | 260 | 8 | 5 | 8 | 9 | 8 |

---

##  Output (Printed in Terminal)

```text
--- TOPSIS RESULTS (Score & Rank) ---
   Topsis Score    Rank
       0.430121     5
       0.651113     1
       0.359169     6
       0.517720     3
       0.447729     4
       0.537590     2
-------------------------------------
Results saved to result.csv
```

---

##  Description

This package helps users evaluate and rank alternatives based on multiple criteria by applying the TOPSIS methodology.  
It is suitable for academic assignments, research work, and real-world decision-making problems.

Before proceeding, make sure of the following points:

1. Correct number of parameters (`inputFileName`, `Weights`, `Impacts`, `resultFileName`).
2. The input file must contain three or more columns.
3. From the 2nd to the last columns, all values must be numeric.
4. The number of weights, number of impacts, and number of criteria columns (from 2nd to last) must be the same.

---

## License
[MIT](https://choosealicense.com/licenses/mit/)
topsis-shubhkarman-102303661 1.0.2
A command-line implementation of TOPSIS for multi-criteria decision making

# 📊 TOPSIS CLI Tool  
**topsis-shubhkarman-102303661**

This package provides a **Python-based command-line implementation of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** method for multi-criteria decision making. It allows users to rank a set of alternatives based on their relative closeness to an ideal best and ideal worst solution using weighted and normalized criteria values.

The tool is designed to be **lightweight, easy to install, and simple to use**, making it suitable for academic projects, data analysis tasks, and real-world decision support systems.

---

## 🚀 Features

- Command-line interface (`topsis` command)
- Supports benefit (`+`) and cost (`-`) criteria
- Automatic validation of input data
- Handles numeric CSV input files
- Generates ranked output file with TOPSIS scores
- Cross-platform support (Windows, macOS, Linux)

---

## 📦 Installation

Install the package using pip:

```bash
pip install topsis-shubhkarman-102303661

## 📦 Command Line Usage

Type the following command in terminal after installation.:

```bash
topsis <InputCSVFile> <Weights> <Impacts> <OutputCSVFile>
equos 3.0.0rc1
Equos.ai official Python SDK.
Requires Python: <4,>=3.11

# Equos Python SDK
[Equos.ai](https://www.equos.ai) official Python SDK.

## Prerequisites
- Go to [Equos Studio](https://studio.equos.ai).
- Create an organization.
- Create an API Key.


## Installation
```bash
pip install equos
```

## Usage

### Setup Equos Client
```py

import os
from equos import Equos


equos_key = os.getenv("EQUOS_API_KEY", "")

const  client = Equos(api_key=equos_key)
```

You don't have an api key ? [Create one here](https://studio.equos.ai)


## Reach Us
- Equos Slack Community: [Join Equos Community Slack](https://join.slack.com/t/equosaicommunity/shared_invite/zt-3d8oy19au-jZpsJB0i~gdL0jbDswdzzQ)
- Support: [Support Form](https://docs.google.com/forms/d/e/1FAIpQLSdoK7LvORdQf7KOQKvhhlESStJcKc3bDB9HPsEet6LuOmVUfQ/viewform)

## Documentation

- Official Documentation: [https://docs.equos.ai](https://docs.equos.ai)
topsis-aviral-102303726 0.1.1
TOPSIS implementation in Python

\# topsis-aviral-102303726



A Python implementation of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method.



\## Features

\- Accepts CSV input file

\- Supports user-defined weights and impacts

\- Computes TOPSIS score and rank

\- Available as CLI tool and Python package



\## Installation

```bash

pip install topsis-aviral-102303726