semvec/pypi-packages huggingface dataset:
topsis-Bhumika 1.0.0
Topsis analysis of a csv file
License: MIT

# Topsis Analysis

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install topsis-Bhumika.

```bash
pip install topsis-Bhumika
```

## Usage

Following query on terminal will provide you the topsis analysis for input csv file.

```
topsisBG -x "dataset-name" -y "w1,w2,w3,w4,..." -z "i1,i2,i3,i4,..."

```

Do not mention the file format, 'csv', as part of dataset-name.

w1,w2,w3,w4 represent weights (w), not already normalised will be normalised later in the code.

i1,i2,i3,i4.... represent information of benefit positive(1) or negative(0) impact criteria.

Note that the first row and first column of dataset is dropped

Rank 1 item denotes the best choice.

## License
[MIT](https://choosealicense.com/licenses/mit/)
topsis-Bhavya 1.0.0
Topsis analysis of a csv file
License: MIT

# Topsis Analysis

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install topsis-Bhavya.

```bash
pip install topsis-Bhavya
```

## Usage

Following query on terminal will provide you the topsis analysis for input csv file.

```
topsisB -a "dataset-name" -b "w1,w2,w3,w4,..." -c "i1,i2,i3,i4,..."

```

Do not mention the file format, 'csv', as part of dataset-name.
w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize. 
Size of w and i is equal to number of features. 

Note that the first row and first column of dataset is dropped

The item attributed rank 1 is the best choice.

## License
[MIT](https://choosealicense.com/licenses/mit/)
kt-toposis 1.0.9
A Python package to get toposis rankings for any table.
License: MIT

### UCS633 Project Submission
* **Name** - *Kartikey Tiwari* 
* **Roll no.** - *101703282* 

# kt-toposis

kt-toposis is a Python package for displaying ranking of all criteria using Topsis technique to get good computational efficiency and ability to measure the relative performance for each alternative in a simple mathematical form. 

## Topsis Description

Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is one of the multi-criteria models in making decision which is known for its  simplicity, rationality, comprehensibility and good computational efficiency. Multi-criteria decision making (MCDM) refers to making choice of the best alternative from among a finite set of decision alternatives in terms of multiple, usually conflicting criteria.

## Getting Started

These instructions will help you to install and use this package for general use. 

## Prerequisites

Your csv file should not have categorical data


## Installation

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

```bash
pip install kt-toposis
```

## Usage
You can import it either in Python IDLE or run directly through command prompt

### For Command Prompt

If you want to use this package on "data.csv" file with 4 columns. You need to change the directory where "data.csv" is stored then. Here -w represents weights which signifies weight of each feature or column in our dataset and -i represents impacts which signifies impact of each column or feature in our data. If a feature is good we will use + to denote else we will use -

```bash
kt-toposis data.csv -w 1 1 1 1 -i + + - +
```
You can use the following command for help

```bash
kt-toposis -h
```

### For Python IDLE

```python
from kt_toposis.topsis import top
top(X,weights,impacts)

#X should be a matrix
#impacts should be a list of string + for positive impact - for negative impact
#weights should be a list of int or float
```
### Sample dataset


|Singer ID	   |Sur     |Taal	|Laaye	|Pitch	|Pace|
| ------------ |:------:|:-----:| -----:|------:|:---:       
|S1	     	   | 0.79   | 0.62	|1.25	|60.89	|11  |
|S2	    	   | 0.66   | 0.44	|2.89	|3.07	|20  | 
|S3	    	   | 0.56   |0.31	|1.57	|62.87	|16  | 
|S4	    	   | 0.82   |0.67	|2.68	|70.19	|16  |
|S5	    	   | 0.75   |0.56	|1.3	|80.39	|20  |

```python
kt-toposis Book1.csv -w 1 1 1 1 1 -i + + + + +
```

### Result

```python
  Topsis Selection
Models     | Rank
-----------------------
1          | 3
2          | 5
3          | 4
4          | 1
5          | 2
Successfully executed
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
Level-Up-RPG 1
This package contains implementation of the game "Level_Up_RPG".
License: MIT

# Level Up RPG

A Python package contains implementation of the game "Level Up RPG".

## Usage

The game "Level Up RPG" is a game where the player plays as long as 
he/she survives in this game. This game is about levelling up as 
much as possible and going as far as possible.