semvec/pypi-packages huggingface dataset:
glowriya-housing 0.0.1
Predicts Median housing value
License: MIT
Keywords: median

Predicts Median housing value

The housing data can be downloaded from https://raw.githubusercontent.com/ageron/handson-ml/master/. The script has codes to download the data. We have modelled the median house value on given housing data. 

The following techniques have been used: 

 - Linear regression
 - Decision Tree
 - Random Forest

Steps performed
 - We prepare and clean the data. We check and impute for missing values.
 - Features are generated and the variables are checked for correlation.
 - Multiple sampling techinuqies are evaluated. The data set is split into train and test.
 - All the above said modelling techniques are tried and evaluated. The final metric used to evaluate is mean squared error.

Change Log
==========

0.0.1 (13-11-2020)
------------------
-First Release
TOPSIS-Ishika-101803017 2.0.0
Implementing TOPSIS
License: MIT

This package is implementation of topsis technique of multi-criteria decision analysis.

You can install this package using following command
pip install TOPSIS-Ishika-101803017

This packages will work on command line interface
> - import TOPSIS-Ishika-101803017 as t
> - t.topsis(InputDataFile, Weights, Impacts, resultfile)
### Input specifications
> - InputDataFile is path to your input csv file(eg inputfile.csv)
> - weights is  string in which each digit represent weight of corresponding column(eg "1,1,1,1")
> - impacts are impacts of column (eg "-,+,+,+" ) 
> - result is name of output file(eg result.csv)

## PRECAUTIONS
> No of weights and no of impacts should be equal to no. of columns in dataset excluding the first column
> Impacts must be either +ve or -ve
> Input file must contain three or more columns
> 2nd to last columns contain numeric values only

Result file contains all the columns of input file and two additional columns having TOPSIS SCORE and RANK

### EXAMPLE

>IF INPUT FILE IS AS FOLLOWS:

| Model | Correlation | RSeq | 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

> OUTPUT FILE:

| Model | Correlation | RSeq | RMSE | Accuracy | Topsis Score | Rank
| ------ | ------ | ----- | ----- | -------- | -------- | --------
| M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.639133 | 2
| M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.212592 | 5
| M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.407846 | 4
| M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.519153 | 3
| M5 | 0.75 | 0.56 | 1.3 | 80.39 | 0.828267 | 1
TOPSIS-Kartik-101803671 2.1.0
A Python package to get TOPSIS result of any data.
License: MIT

# topsis
It is a Python package to find out the best value among the different data using the mathematical calculations.
## Installation

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

```bash
pip install TOPSIS-Kartik-101803671
```

## About
TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' and is a pretty straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution and comparing the distance of each one of the alternatives to those

To get started quickly, just use the following:

```bash
from topis_pckg.topsis import topsis
topsis('inputfilename','Weights','Impacts','Outputfilename')
```
make ensure the weights and impacts should be in "" 

eg: "1,1,1,1" and "+,-,+,-"

## Pre-requisite
The data should be enclosed in the csv file. There must be more than 2 columns


## Result
the output(outputfilename)  is saved in the project folder with extra 2 columns with topsis score and rank.
TOPSIS-YASHPAL-101803611 5.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python



Submitted By: **Yashpal 101803611**

***

## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-Yashpal-101803611  can be run as in the following example:

This package is used only on command line with keyword topsis followed by data file name, weights, impacts and file name in which result is stored.Output is stored in the file given in arguments along with data given.

### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+" final.csv
```

### 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 (`w`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `I`.
```
<br>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    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-Samridhi-101803011 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# Topsis_Samridhi_101803011
# TOPSIS


Submitted By: **Samridhi Sehgal**

***

## 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-Samridhi-101803011
```


### 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**