semvec/pypi-packages huggingface dataset:
Topsis-Rakshita-102303498 1.0.4
TOPSIS implementation for MCDM problems
License: MIT

# Topsis-Rakshita-102303498

## Project Description

**Topsis-Rakshita-102303498** is a Python package that implements the  
**Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)**,  
a widely used method for **Multiple Criteria Decision Making (MCDM)**.

This package allows users to rank alternatives based on multiple criteria by
providing a CSV file, weights, and impacts through the command line.





## What is TOPSIS?

TOPSIS is based on the concept that the best alternative should have:
- the **shortest distance from the ideal solution**, and
- the **farthest distance from the negative-ideal solution**.



## Installation

Install the package from PyPI using pip:

```bash
pip install Topsis-Rakshita-102303498
```

## Usage
Run the package from the command line:
``` bash
topsis input.csv "1,1,1,1" "+,+,-,+" output.csv
```
## Arguments 
``` bash
input.csv : CSV file containing alternatives and criteria
weights : Weights for each criterion (comma-separated)
impacts : Impact of each criterion (+ for benefit, - for cost)
output.csv: Output CSV file containing TOPSIS scores and ranks
Input File Format
First column: Alternatives
Remaining columns: Numeric criteria values
No categorical data allowed in criteria columns
```

# Example Input (sample.csv)
``` bash
Model,Storage,Camera,Price,Looks
M1,16,12,250,5
M2,16,8,200,3
M3,32,16,300,4
M4,32,8,275,4
M5,16,16,225,2
```
## Output

The output file will contain two additional columns:
Topsis Score
Rank
Higher score indicates a better alternative.

## Notes
The first column is treated as an identifier and not used for calculations.
All criteria values must be numeric.
Weights and impacts must match the number of criteria columns.

## License
This project is licensed under the MIT License.