semvec/pypi-packages huggingface dataset:
22BEE0039 0.0.1
A sample package from Sameer Bisht.
Requires Python: >=3.7

#Hello world

This is an example project created by Sameer Bisht VIT Vellore. 22BEE0039
This contains two modules.
One module contains functions. and the other uses the functions from the first.

## Installation
Run the following to install:
'''python
pip install sameer22BEE0039
'''
TOPSIS-KHUSHI-102016099 1.2.2
Topsis package
Requires Python: >=3.6

# Topsis Description
This python package can help you to calculate topsis score and corresponding rank in maximum 2-3 lines of code.
# SUBMITTED BY:
KHUSHI SRIVASTAVA

102016099

3CS12
# INSTALLATION
 pip install TOPSIS-KHUSHI-102016099
# import 
from TOPSIS_KHUSHI_102016099 import topsis 
# Usage
Call the Topsis function to generate the csv file in which 1st parameter is your input dataframe, 2nd is weights, 3rd is impacts and last is the name of output csv file. For eg:
topsis("102016099-data.csv","1,2,1,2,1","+,-,+,+,-","102016099-result.csv")
# Constraints
1) Input dataframe with 1st column as model name and rest are it's features.
2) Weights must be commas seperated and in the form of a string.
3) Impacts must be commas seperated and in the form of a string having only positive and negative sign.
4) Input file must contain 3 or more columns
5) First column is the object or variable name
6) From 2nd to last columns contain numeric values only

# Output
1) Result file contains all the columns of input file and two additional columns having the topsis score and the rank

# License
Copyright 2023

Khushi Srivastava

This repository is licensed under the MIT License
Topsis-Saksham-10200353 0.0.2
A package -> Calculates Topsis Score and Ranks them accordingly


## Topsis_Saksham Yadav - 102003673

# TOPSIS

Submitted By: **Saksham Yadav - 102003673**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **0.0.2**.

Date: **2023-01-22**.

Author: **Saksham Yadav**.

Maintainer: **Saksham Yadav <syadav1_be20@thapar.edu>**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## 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 install this package:

```
>> pip install Topsis-Saksham_Yadav-102003673
```

### In Command Prompt

```
>> python <program.py> <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

## Input file (data.csv)

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 (`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 file (result.csv)

| 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 output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**