semvec/pypi-packages huggingface dataset:
housing-library-5494 0.1
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training


# README.md

## Median housing value prediction
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.

## To execute the script
```python nonstandardcode.py```

## To activate conda environment
```conda activate mle-dev```

## To create environment from yml file
```conda env create --name mle-dev --file=env.yml```

## Command to install isort in conda environment
```conda install isort```

## Command to install black in conda environment
```conda install black```

## Command to install flake8 in conda environment
```conda install flake8```

## Command to refactor python code with isort 
``` isort nonstandardcode.py```

## Command to refactor python code with black
``` black nonstandardcode.py```

## Command to refactor python code with flake8
``` flake8 nonstandardcode.py```
skleanr 3.3.9
The sklearn module, often referred to as scikit-learn, is a widely-used Python library for machine learning. It offers a comprehensive set of tools and algorithms for various machine learning tasks, including classification, regression, clustering, dimensionality reduction, model evaluation, data preprocessing, and more.
Requires Python: >=3.6

The "sklearn" module is a reference to scikit-learn, which is one of the most popular and widely used machine learning libraries in the Python ecosystem. Scikit-learn provides a comprehensive set of tools for various machine learning tasks, including data preprocessing, model selection, training, evaluation, and deployment.
bluetooth-mesh 0.8.6
Bluetooth mesh for Python
Requires Python: >=3.6.0,<3.11.0

=====================
python-bluetooth-mesh
=====================

.. image:: https://img.shields.io/pypi/v/bluetooth-mesh.svg
    :target: https://pypi.org/project/bluetooth-mesh
    :alt: PyPI version

.. image:: https://img.shields.io/pypi/pyversions/bluetooth-mesh.svg
    :target: https://pypi.org/project/bluetooth-mesh
    :alt: Python versions

.. image:: https://app.travis-ci.com/SilvairGit/python-bluetooth-mesh.svg?branch=master
    :target: https://travis-ci.com/github/SilvairGit/python-bluetooth-mesh
    :alt: See Build Status on Travis CI

.. image:: https://readthedocs.org/projects/python-bluetooth-mesh/badge/?version=latest
    :target: https://python-bluetooth-mesh.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

Bluetooth mesh SDK for Python allows developing applications communicating with
Bluetooth mesh network using BlueZ's bluetooth-meshd.

----

What is this thing?
-------------------

https://www.bluetooth.com/specifications/mesh-specifications

https://git.kernel.org/pub/scm/bluetooth/bluez.git/tree/doc/mesh-api.txt


Installation
------------

You can install "python-bluetooth-mesh" via `pip`_ from `PyPI`_::

    $ pip install bluetooth-mesh


Contributing
------------
Contributions are very welcome. Tests can be run with `tox`_, please ensure
the coverage at least stays the same before you submit a pull request.


License
-------

Distributed under the terms of the `GPL-2.0`_ license, "python-bluetooth-mesh" is
free and open source software


Issues
------

If you encounter any problems, please `file an issue`_ along with a detailed description.

.. _`GPL-2.0`: http://opensource.org/licenses/GPL-2.0
.. _`file an issue`: https://github.com/SilvairGit/python-bluetooth-mesh/issues
.. _`pytest`: https://github.com/pytest-dev/pytest
.. _`tox`: https://tox.readthedocs.io/en/latest/
.. _`pip`: https://pypi.org/project/pip/
.. _`PyPI`: https://pypi.org/project
critical-node 0.1.0
Used to identify key nodes for influential paper writing
Requires Python: >=3.6

# **1. 内置数据集**

## **1.1 Network**

### **1.1.1 数据概述**

### **1.1.2 网络简介**

### **1.1.3 快速上手API**

## **1.2 SIR**

# **2. critical_node API**

## **2.1 DATA**

### **2.1.1 Inlay**
#### **2.1.1.1 All_Data_Set**
- 简介：该函数用于获取该库中所以内置数据集的名称
- 快速上手：<code>
% 快速获取库内的所有内置的网络名称
from src.critical_node.DATA.Inlay import All_Data_Set
AllData = All_Data_Set()
print(AllData)</code>

### **2.1.2 Get_Data**
#### **2.1.2.1 Get_Single_BuilInNetwork**
- 简介：获取某个网络的点边数据集合
- return: [[node1,node2],[node2,node3],......]
- 快速上手：<code>
% 快速获取as网络的数据列表
from src.critical_node.DATA.Get_Data import Get_Single_BuilInData
print(Get_Net_Data('as'))</code>
#### **2.1.2.2 Get_All_BuilInNetwork**
- 简介：获取内置数据库内的所有数据的字典
- return: {BuilInData:[[node1,node2],......],......}
- 快速上手：<code>
% 快速获取内置所有网络的数据字典
from src.critical_node.DATA.Get_Data import Get_All_BuilInData
print(Get_All_BuilInData())</code>

## **