semvec/pypi-packages huggingface dataset:
Topsis-Nimish-102017084 1.0.1
The module, topis requires pandas, math and sys modules to function This module is used to print the score and rank after using TOPSIS (Technique for Order Preference based on Similarity to Ideal Solution)

Submitted by:   
Name: **Nimish Lakhmani**   
Roll No: **102017084**  
Group: **3CS4** 





              


## Installation

Install this package in the system using [pip](https://pip.pypa.io/en/stable/) command.

bash
  pip install Topsis-Nimish-102017084





## Usage

* Import the installed library using the following command.

bash
  import Topsis

You can import it using the first word coming before hypen (-) in library's name as shown above.

* Enter the three parameters in three lines:
  * .csv filename, followed by .csv extension
  * values of weights, each separated by comma(,)
  * values of impacts, either '+' or '-', each separated by comma(,) 
  
bash
  sample.csv
  0.25,0.25,0.25,0.25
  -,+,+,+




## Example of input & output

### Input:

* sample.csv file, depicts the dataset of mobile phones having varying features.

| Model | Price (in $) | Storage Space (in GB) | Camera (in MP)| Looks | 
| :---------------: | :---------------: | :---------------: | :---------------: | :---------------: |
| M1 | 250 | 16 | 12 | Excellent |
| M2 | 200 | 16 | 8 | Average |
| M3 | 300 | 32 | 16 |  Good |
| M4 | 275 | 32 | 8 |  Good |
| M5 | 225 | 16 | 16 |  Below Average |

* weights = [0.25,0.25,0.25,0.25]  
* impacts = [-,+,+,+]

### Output:

| Model | Price (in $) | Storage Space (in GB) | Camera (in MP)| Looks | Topsis Score | Rank | 
| :---------------: | :---------------: | :---------------: | :---------------: | :---------------: | :---------------: | :---------------: |
| M1 | 250 | 16 | 12 | Excellent | 0.526983 | 3 |
| M2 | 200 | 16 | 8 | Average | 0.190599 | 5 |
| M3 | 300 | 32 | 16 |  Good | 0.809401 | 1 |
| M4 | 275 | 32 | 8 |  Good | 0.688168 | 2 |
| M5 | 225 | 16 | 16 |  Below Average | 0.422630 | 4 |





## Important Points

* The first column is not considered while solving the MCDM Problem. Make sure the csv file follows the format as shown in sample.csv.
* Any column (from 2nd to last) containing categorical values is converted into numeric column .

## License

[MIT](https://choosealicense.com/licenses/mit/)