semvec/pypi-packages huggingface dataset:
housing-library-5492 0.1
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training

# 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 excute the script
python < scriptname.py ># mle-training
mle-training
housing-library-5497 0.1
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training

# mle-training

# 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 excute the script
### conda create --name <env-name> biopython
  - script for creating a new environment

### conda activate <env-name>
 - activating the new environment

 - installing necessary packages

### python < scriptname.py >
 - command to run the script

### conda env export <env-name> > <filename.yml>
 - exporting environment

### conda activate
  - changing the environment to default base
housing-library-5520 0.1
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training

# mle-training-
# 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 excute the script
python < scriptname.py >
## Command to create Virtual Enviornemnt:
conda --version
conda create --name mle-dev biopython
conda activate mle-dev

## install the necessary librabries like numpy,pandas , matplotlib and scikit learn
conda install numpy
conda install pandas
conda install matplotlib

## To excute the script
python < scriptname.py >
python nonstandardcode.py

## Exporting the enviorment
conda export --name MLE-training >env.yml
housing-library-5495 0.1
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training

# mle-training
# 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 Create a conda environment from new_env.yml file
```env_from_yml
conda env create -f new_env.yml
```

## To activate the created environment
```
conda activate mle-dev
```
## To excute the script
```shell
python nonstandardcode.py
```
kaftarsdk 0.0.3
a simple sdk to send notifications on queue for celery tasks
Requires Python: >=3.10

# Kaftar SDK

## Usage

Package installation

``` bash
pip install kaftarsdk
```

Here is how to set a multiple notification task on Rabbitmq with client package:

``` python
from kaftarsdk import Notification


app = Notification('app_name')
recipient = [
    {'receiver': 'user@exampe.com', 'uuid': 'b60bf22b-6df8-439b-ba6f-73f203e692d1'},
    {'receiver': '+9893002220022', 'uuid': 'b60bf22b-6df8-439b-ba6f-73f203e692d1'}
]
app.send_notification(
    {
        'subject': 'subject 1',
        'content': 'sample 2'
    },
    recipient,
    # send on timestamp
    "1696421378")

```
kaftar-sdk 0.0.3
a simple sdk to send notifications on queue for celery tasks
Requires Python: >=3.10

# Kaftar SDK

## Usage

Package installation

``` bash
pip install kaftar_sdk
```

Here is how to set a multiple notification task on Rabbitmq with client package:

``` python
from kaftar_sdk import Notification


app = Notification('app_name')
recipient = [
    {'receiver': 'user@exampe.com', 'uuid': 'b60bf22b-6df8-439b-ba6f-73f203e692d1'},
    {'receiver': '+9893002220022', 'uuid': 'b60bf22b-6df8-439b-ba6f-73f203e692d1'}
]
app.send_notification(
    {
        'subject': 'subject 1',
        'content': 'sample 2'
    },
    recipient,
    # send on timestamp
    "1696421378")

```
fibers-ddtest 1.2.0
Lightweight cooperative microthreads for Pyhton

===========================================
fibers: lightweight concurrent multitasking
===========================================

.. image:: https://badge.fury.io/py/fibers.png
    :target: http://badge.fury.io/py/fibers

Overview
========

Fibers are lightweight primitives for cooperative multitasking in Python. They
provide means for running pieces of code that can be paused and resumed. Unlike
threads, which are preemptively scheduled, fibers are scheduled cooperatively,
that is, only one fiber will be running at a given point in time, and no other
fiber will run until the user explicitly decides so.

When a fiber is created it will not run automatically. A fiber must be 'switched'
into for it to run. Fibers can switch control to other fibers by way of the `switch`
or `throw` functions, which switch control or raise and exception in the target
fiber respectively.

Example:

::

    import fibers

    def func1():
        print "1"
        f2.switch()
        print "3"
        f2.switch()

    def func2():
        print "2"
        f1.switch()
        print "4"

    f1 = fibers.Fiber(target=func1)
    f2 = fibers.Fiber(target=func2)
    f1.switch()


The avove example will print "1 2 3 4", but the result was obtained by the
cooperative work of 2 fibers yielding control to each other.


CI status
=========

::target: https://github.com/saghul/python-fibers/actions/


Documentation
=============

http://readthedocs.org/docs/python-fibers/


Installing
==========

fibers can be installed via pip as follows:

::

    pip install fibers


Building
========

Get the source:

::

    git clone https://github.com/saghul/python-fibers


Linux:

::

    ./build_inplace

Mac OSX:

::

    (XCode needs to be installed)
    export ARCHFLAGS="-arch x86_64"
    ./build_inplace

Microsoft Windows:

::

    python setup.py build_ext --inplace


Running the test suite
======================

The test suite can be run using nose:

::

    nosetests -v


Author
======

Saúl Ibarra Corretgé <saghul@gmail.com>

This project would not have been possible without the previous work done in
the `greenlet <http://greenlet.readthedocs.org>`_ and stacklet (part of
`PyPy <http://pypy.org>`_) projects.


License
=======

Unless stated otherwise on-file fibers uses the MIT license, check LICENSE file.


Supported Python versions
=========================

Python >= 3.5 are supported. Other older Python versions might work, but
they are not actively tested. CPython and PyPy are supported.


Supported architectures
=======================

x86, x86-64, ARM, MIPS64, PPC64 and s390x are supported.


Contributing
============

If you'd like to contribute, fork the project, make a patch and send a pull
request. Have a look at the surrounding code and please, make yours look
alike.
housing-library-5493 0.1
Sample code for coding practice
Requires Python: >=3.11
Keywords: housing,data training

# MLE-training

# 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 excute the script
python < scriptname.py >