semvec/pypi-packages huggingface dataset:
courierman 0.1.1
Courier Man - A Python interface for Postman
License: MIT
Keywords: postman

# Courier Man

I've started this because I was unable to find a working library for handling postman collections inside Python code.

The main objetive of this project is, given a Json exported from a postman library, the hability to load it, find requests, change any field and execute it through the requests library.

## Name

Since all common names were taken, I took a similar word to post: courier, which surely is not related to HTTP Post. Hopefully the name will not create so much confusion.
TOPSIS-101803006 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Priyanka 101803006**

***

## 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 use this package:

TOPSIS-PriyankaGupta-101803006  can be run as in the following example:



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

### In Python IDLE:
```
>>> import pandas as pd
>>> from src.topsis import topsis
>>> dataset = pd.read_csv('data.csv')
>>> data=dataset.iloc[ :,1:].values.astype(float)
>>> weights = [1,1,1,1]
>>> impacts = ["+" , "+" , "-" , "+" ]
>>> topsis(data,weights,impacts)
```

<br>

## Sample dataset

The decision matrix (`a`) 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


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 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.
TOPSIS-Priyanka 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Priyanka 101803006**

***

## 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 use this package:

TOPSIS-PriyankaGupta-101803006  can be run as in the following example:



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

### In Python IDLE:
```
>>> import pandas as pd
>>> from src.topsis import topsis
>>> dataset = pd.read_csv('data.csv')
>>> data=dataset.iloc[ :,1:].values.astype(float)
>>> weights = [1,1,1,1]
>>> impacts = ["+" , "+" , "-" , "+" ]
>>> topsis(data,weights,impacts)
```

<br>

## Sample dataset

The decision matrix (`a`) 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


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 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.