semvec/pypi-packages huggingface dataset:
topsis-sukham-102003334 0.1
A Python package implementing TOPSIS technique.
License: MIT

# Topsis_Sukham_102003334
This is a python package used to implement TOPSIS(Technique of Order Preference Similarity to the Ideal Solution) for MCDA(Multiple criteria decision analysis)

<br>

## How to use this package:

pip install topsis-sukham-102003334

### In Command Prompt
```
>> topsispy data.csv "1,1,1,1" "+,+,-,+" out.csv
```
## Sample dataset

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

## Output
This will be in our Output csv file
Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | TOPSIS SCORE | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------ | ------------
M1 |	0.79 | 0.62	| 1.25 | 60.89 | 0.77 | 2.0
M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.23 | 5.0
M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.44 | 4.0
M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.52 | 3.0
M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.81 | 1.0
Topsis-Saksham-102003039 1.0.0
Topsis package for Multiple Criteria Decision Making problems(MCDM) problems
License: MIT
Requires Python: >=3.6

# Decision Making Using TOPSIS

_By: **Saksham Khetarpal**_
_Roll no: **102003039**_

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria-based decision-making method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Saksham-102003039.

```bash
pip install Topsis-Saksham-102003039
```

## Usage

Enter csv input filename followed by _.csv_ extentsion, then enter the _weights_ with values separated by commas, followed by the _impacts_ with comma separated signs _(+,-)_ and finally the name of the output file followed by _.csv_ extension.
```bash
topsis input.csv "1,1,1,1" "+,+,-,+" output.csv
```

## Example



A csv file showing data for different mobile handsets having varying features (input.csv).

| Model  | Storage Space(gb) | Camera(MP)| Price($)  | Looks(out of 5) |
| :----: |:--------------------:|:------------:|:------------:|:---------------:|
| 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 |

weights = "1,1,1,1"

impacts ="+,+,-,+"

## Input:

```python
topsis input.csv "1,1,1,1" "+,+,-,+" output.csv
```

## Output:
The output is a csv file (output.csv) with the following 2 columns appended to the original input file.
```
 Topsis Score  Rank
1   0.534277     3
2   0.308368     5
3   0.691632     1
4   0.534737     2
5   0.401046     4

``` 



## License
[MIT](https://choosealicense.com/licenses/mit/)