semvec/pypi-packages huggingface dataset:
gloria-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
qmss-sl-practice 0.1.0
This is a practice package.
License: MIT
Requires Python: >=3.7,<4.0

# qmss-sl-practice 

![](https://github.com/shiyinglai/qmss_sl_practice/workflows/build/badge.svg) [![codecov](https://codecov.io/gh/shiyinglai/qmss_sl_practice/branch/main/graph/badge.svg)](https://codecov.io/gh/shiyinglai/qmss_sl_practice) ![Release](https://github.com/shiyinglai/qmss_sl_practice/workflows/Release/badge.svg) [![Documentation Status](https://readthedocs.org/projects/qmss_sl_practice/badge/?version=latest)](https://qmss_sl_practice.readthedocs.io/en/latest/?badge=latest)

This is a practice package.

## Installation

```bash
$ pip install -i https://test.pypi.org/simple/ qmss_sl_practice
```

## Features

- TODO

## Dependencies

- TODO

## Usage

- TODO

## Documentation

The official documentation is hosted on Read the Docs: https://qmss_sl_practice.readthedocs.io/en/latest/

## Contributors

We welcome and recognize all contributions. You can see a list of current contributors in the [contributors tab](https://github.com/shiyinglai/qmss_sl_practice/graphs/contributors).

### Credits

This package was created with Cookiecutter and the UBC-MDS/cookiecutter-ubc-mds project template, modified from the [pyOpenSci/cookiecutter-pyopensci](https://github.com/pyOpenSci/cookiecutter-pyopensci) project template and the [audreyr/cookiecutter-pypackage](https://github.com/audreyr/cookiecutter-pypackage).
TOPSIS-Aditi-101803650 0.2
Pyhon Package for TOPSIS score
License: MIT

# TOPSIS_Aditi_101803650
With this you can calculate the TOPSIS score and RANK of the data provided in '.csv' format.
- Input file:
  - contain three or more columns
  - First column is the object/variable name.
  - From 2nd to last column contain numeric values only

# Overview
  - You can check intermediate steps as well as the final score i.e it provides functions to calculate normalized matrix, weight normalized decision matrix , ideal best , ideal worst lists and so on.

## Usage

In the following paragraphs, I am going to describe how you can get and use TOPSIS for your own projects.

### Getting it
To download TOPSIS, either fork this github repo or simply use Pypi via pip.

    $ pip install TOPSIS_Aditi_101803650

### Using it
TOPSIS was programmed with ease-of-use in mind. Just, import topsis from TOPSIS-Aditi-101803650

    from TOPSIS_Aditi_101803650 import topsis

And you are ready to go! 

## topsis
There are 5 functions in this:
  - normalized_matrix
  - weight_normalized
  - ideal_best_worst
  - euclidean_distance
  - topsis_score

### normalized_matrix()
This function takes location of the input file as parameter and returns normalized decision matrix.

### weight-normalized()
This function takes location of the input file as well as the weights corresponding to columns in file. No. of weights should be equal to the no. of columns(from 2nd to last column). It returns weight normalized decision matrix.

### ideal_best_worst()
This takes location of input file, weights and impacts as parameters. Impact should either be "+" or "-" and should be separated by ",". No. of impacts should also be equal to the no. of columns(from 2nd to last column). It return two lists: one with ideal best values and the other with ideal worst values corresponding to every column.

### euclidean_distance()
This takes same input as that of ideal_best_worst() function but returns two lists:

  1) Euclidean distance from ideal best
  2) Euclidean distance from ideal worst
  
### topsis_score()
This also takes the same input as the previous function but returns matrix with the same columns as input file and two more columns named 'Topsis Score' and 'Rank'.

So any function can be used in the below format:

    topsis.function_name(parameters)
TOPSIS-Aseem-101803469 3.0.2
A Python package for implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Aseem Khullar**

***

## 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-Aseem-101803469
```


### In Command Prompt
```
>> topsis data.csv "1,1,2,1" "+,+,-,+" 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 | 0.849592 | 2
M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.143187 | 5
M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.597633 | 3
M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.364575 | 4
M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.877204 | 1

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**