semvec/pypi-packages huggingface dataset:
Topsis-Jai-102003457 1.0.0
A package -> Calculates Topsis Score and Rank them accordingly
License: MIT

## Topsis_Jai

# TOPSIS

Submitted By: **JAI MEHROTRA-102003457**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0.0**.


Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## What is TOPSIS?

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,
and greatest distance from the negative-ideal solution.

<br>

## How to install this package:

```
>> pip install TOPSIS-Yash-101803064
```

### In Command Prompt

```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy |
| ----- | ----------- | ------------- | ---- | -------- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    |

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank |
| ----- | ----------- | ------------- | ---- | -------- | ------------ | ---- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    | 0.7722       | 2    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    | 0.2255       | 5    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    | 0.4388       | 4    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    | 0.5238       | 3    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    | 0.8113       | 1    |

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
Topsis-Sidharth-102003395 0.1
Implements topsis on the given input data file and generates topsis score and ranks accordingly.
License: MIT

## Topsis-Sidharth-102003395
Topsis implementation in python. Used as a command line tool to generate topsis score and ranks of the given input data file along with weights and impacts.

## Installation
``` pip install Topsis-Sidharth-102003395```

## How to use it?
Open the terminal / cmd and type topsis <input_filename.csv> <"Weights"> <"Impacts"> <output_filename.csv>

## License
© 2023 Sidharth

This repository is licensed under the MIT license. See LICENSE for details.
topsis-kartik-102003565 1.0.1
topsis
License: MIT
Keywords: topsis,MCDM

# TOPSIS Package in Python

Submitted by: Kartik Madan

Roll no: 102003565

---

## TOPSIS

TOPSIS is an acronym that stands for Technique of Order Preference Similarity to the Ideal Solution and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those.

---

## How to use

The package topsis-kartik-102003565 can be run though the command line as follows:

```
>> pip install topsis-kartik-102003565
```

```
>>python topsis data.csv "1,1,1,1" "+,+,-,+" result
```

## Sample Input

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.

<table><thead><tr><th>Fund Name</th><th>P1</th><th>P2</th><th>P3</th><th>P4</th></tr></thead><tbody><tr><td>M1</td><td>250</td><td>16</td><td>12</td><td>5</td></tr><tr><td>M2</td><td>200</td><td>16</td><td>8</td><td>3</td></tr><tr><td>M3</td><td>300</td><td>32</td><td>16</td><td>4</td></tr><tr><td>M4</td><td>275</td><td>32</td><td>8</td><td>4</td></tr><tr><td>M5</td><td>225</td><td>16</td><td>16</td><td>2</td></tr></tbody></table>

<br>
Weights `weights` is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.
<br>

## Sample Output

The output that generated from the following input will be:

<table><thead><tr><th>Model</th><th align="right">P1</th><th align="center">P2</th><th>P3</th><th>P4</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">250</td><td align="center">16</td><td>12</td><td>5</td><td>0.534277</td><td>3</td></tr><tr><td>M2</td><td align="right">200</td><td align="center">16</td><td>8</td><td>3</td><td>0.308368</td><td>5</td></tr><tr><td>M3</td><td align="right">300</td><td align="center">32</td><td>16</td><td>4</td><td>0.691632</td><td>1</td></tr><tr><td>M4</td><td align="right">275</td><td align="center">32</td><td>8</td><td>4</td><td>0.534737</td><td>2</td></tr><tr><td>M5</td><td align="right">225</td><td align="center">16</td><td>16</td><td>2</td><td>0.401046</td><td>4</td></tr></tbody></table>

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank** .
Here the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 5 is the worst solution.

---