semvec/pypi-packages huggingface dataset:
topsis-ashikothari10 0.0.1
A small data preprocessing package
Requires Python: >=3.6

A data pre-processing approach applied on a small data to predict the ranks of the model. This is a simple package to use topsis(technique for Order of preference by Similarity to Ideal Solution) approach to select best among many things based on different attributes, such as selecting the best Machine Learning algorithms based on correlation,R-square,root mean square error and accuracy. You can use this simple package to get ranks on basis of topsis approach, ypu simply need to pass NumPy array after pre-processing your data along with weights between 0 and 1 and all weights should sum to 1 to get correct results and you need to pass impacts as well in form of '+' or '-' where '+' indicates that you need to maximise value of that particular column and '-' indicates that you need to minimise value of that particular column. After passing all these parameters you will get your row rank.
topsis-RajatGupta-101703427 1.0.0
A python package to identify the best model out of different models using TOPSIS
Requires Python: >=3.6

# Ranking System Using Topsis

**Project 1 : UCS633**


Submitted By: **Rajat Gupta 101703427**

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Ranking system.

```bash
pip install topsis-RajatGupta-101703427
```

<br>

## How to use this package:

topsis-RajatGupta-101703427 can be run as done below:



### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+"
```
<br>

### In Python IDLE:
```python
>>> import pandas as pd
>>> import topsis
>>> data = pd.read_csv('data.csv').values
>>> data = data[:,1:]
>>> w = [1,1,1,1]
>>> impacts = ["+" , "+" , "-" , "+" ]
>>> topsis.topsis(data,w,impacts)
```

<br>

## Sample dataset

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 list 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

```
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1
```
<br>

The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.