semvec/pypi-packages huggingface dataset:
102003037-topsis 0.0.1
Topsis
Keywords: python,topsis


# 102003037 TOPSIS PACKAGE HIMANGI SHARMA
Roll Number : 102003037 <br>
Subgroup : 3COE18 <br>
The program takes csv file containing our data to be ranked, weights and impacts in the form of "+" or "-", seperated by commas as inputs and then outputs a resultant csv file with two additional columns of performance score and Ranks.

# What is TOPSIS
TOPSIS, Technique of Order Preference Similarity to the Ideal Solution, is a multi-criteria decision analysis method (MCDA). <br>
It chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution. <br>

## Installation
### How to install the TOPSIS package <br>
using pip install:-<br>
``` pip install 102003037-topsis-Himangi ```

## For Calculating the TOPSIS Score
Open terminal and type <br>
``` 102003037 102003037-data.csv "1,1,1,1" "+,+,-,+" 102003037-output.csv ```

The output will then be saved in a newly created CSV file whose name will be provided in the command line by the user.

## Input File [102003037-data.csv]:
Topsis mathematical operations to be performed on the input file which contains a dataset having different fields.

## Weights ["1,1,1,1"]
The weights to assigned to the different parameters in the dataset should be passed in the argument, seperated by commas.

## Impacts ["+,+,-,+"]:
The impacts are passed to consider which parameters have a positive impact on the decision and which one have the negative impact. Only '+' and '-' values should be passed and should be seperated with ',' only.

## Output File [102003037-output.csv]:
This argument is used to pass the path of the result file where we want the rank and performance score to be stored.
Topsis-Vaibhav-102003423 0.2
Doing Evaluation of alternatives based on multiple criteria using TOPSIS method.

## Topsis_Vaibhav_Verma

# TOPSIS

Submitted By: **Vaibhav Verma-102003423**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0.4**.

Date: **2020-11-11**.

Author: **Vaibhav Verma**.

Maintainer: **Vaibhav Verma <vverma_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-Vaibahav-102003423
```

### In Command Prompt

```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## 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**
BDevManager 0.0.4
A business development manager library

andrew_bdm from leadlist 
import LeadList from leadsource 
import LeadSource from top100prospectleads 
import Top100ProspectLeads from top10targetsfortheweek 
import Top10TargetsForTheWeek from todaysactivity 
import TodaysActivity 
from TodayTarget import TodayTarget 
from game_loop import GameLoop 
from bant import BANT

leads = LeadList("sales_leads.csv") 
top_leads = Top100ProspectLeads() 
top_leads.add_leads(leads.process_leads()) 
top_10_targets = Top10TargetsForTheWeek() 
top_10_targets.add_leads(top_leads.get_leads()) 
today_target = TodayTarget(top_10_targets.get_leads()) 
todays_activity = TodaysActivity() 
bant = BANT() game_loop = GameLoop(today_target, todays_activity, bant) 
game_loop.start()