semvec/pypi-packages huggingface dataset:
tweetpy 1.0.4
Python package that allows to access the Tweet data without API
License: MIT
Keywords: twitter,tweets,tweet,twitter_scraper,twitter_sentiment,status

# tweetpy
Python package which to access the Tweet data without API

## Installation

The easiest way to install the latest version from PyPI is by using pip:
```
pip install tweetpy
```

## Usage

Download it by clicking the green download button here on Github. You only need to parse argument specific Twitter keyword.
```python
>>> from tweetpy.tweet import Tweet
>>> tweet = Tweet(1567531687130632199)
```
## Documentation

| ATTRIBUTE      | DESCRIPTION	 | VALUE      | EXAMPLE |
| ----------- | ----------- | ----------- | ----------- |
| `text` | get tweet text | String   | "Welcome to the home" |
| `conversation_count` | get conversation tweets count | Integer | 0 |
| `lang` | get tweet language code | String   | "en" |
| `favorite_count` | get likes count | Integer | 195 |
| `created_at` | get when the tweet created | datetime.datetime | datetime.datetime(2018, 8, 3, 2, 22, 56) |
| `id` | get tweet id | Integer  | 1025205344660856838 |
| `id_str` | get tweet id as string | String   | '1025205344660856838' |
| `possibly_sensitive` | is tweet has sensitive content | Boolean | False |
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python  -m venv venv
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pip install wheel setuptools twine
python setup.py sdist bdist_wheel
python -m twine upload dist/*
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topsis-VardaanKhosla-102003295 1.0.0
A Python package implementing TOPSIS technique used to solve the multi criteria decision making problems.
License: MIT

# TOPSIS-Python

**Assignment 1 : UCS654 Predictive Analysis using Statistics**

**Author - Vardaan Khosla(102003295), TIET Patiala**

## 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>

## This is how we can use the developed package:

Topsis-VardaanKhosla-102003295 package can be run as follows:

### In the Command Prompt type:

```
>> topsis input_data.csv "0.25,0.25,0.25,0.25" "+,+,-,+" output_data.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.The number of entries in the impacts and weights column must be equal to the number of columns starting from the 2nd column in the input dataset. For example, in this case there are 4 columns consisting of parameters which affect the decision making procedure. Each column will have an according weight and impact associated to it.

| 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

The output will consist of the original csv file along with two additional columns Topsis Score and Rank.

```
Model  Topsis Score    Rank
-----  ------------    ----
  1    0.77221          2
  2    0.225599         5
  3    0.438897         4
  4    0.523878         3
  5    0.811389         1 
```

<br>