semvec/pypi-packages huggingface dataset:
MLOne 0.0.51
A Python package with which users can just drop their dataset and download the best ML model for their dataset

This package enables you to directly fit the best Machine Learning Model for your dataset by automating all the preprocessing and model fitting steps, Additionally it also performs Exploratory data analysis in the dataset.

Code Snippet:
Auto_Fit(File_Path, bias_var=False)

Parameter:
bias_var: {True, False}(Default: False)
Calculates average Bias, Variance and Expected Loss for all the models.


Rules and guide lines for uploading the dataset:
1. The file should be either .csv or .xlsx
2. Number of columns : 3 < cols > 100
3. Number of rows : 200 < rows > 2500
4. The index col must be the first column.If the dataset doesn't have an index column include it.For example,you can use row number as index.
5. The dependent variable or the target class should be the last column

Model default settings:
chi square Test
p val < 0.1 

Train Test Validation split ratio ** 70:20:10
SSS No.of folds ** 10

Random search params
scores = AUC,precision,accuracy
refit criterion = AUC

KNN params:
2 < n_neighbors < 5
metric = euclidean,manhattan,minkowski

Logistic Regression:
penalty = l1,none
solver = default
c = 0.1 geomspace,no.of elements =3

SVC
params = {'C' : [1,10,100], 'kernel' : ['rbf', 'linear'], 'gamma' : ['scale', 'auto']}

Random Forest Classifier
params = {'n_estimators' : [10,100,200], 'criterion' : ['gini', 'entropy']}

Decision Trees
params = {'criterion' : ['gini', 'entropy'], 'splitter' : ['best', 'random']}

Naive Bayes(Gaussian)
default parameters
lambdaChaining 1.0.0
A module for chaining lambdas
Requires Python: >=3.6

# lambdaChaining
##### *lambdaChaining is a package for chaining lambdas*

---

## __Instillation__
    python -m pip install lambdaChaining

## __Lambdas__

#### *bind*
* ##### ALIASES: [b, binding, binding_lambda]

* ##### PARAMETERS: x -> LAMBDA: EXPRESSION, f=(lambda x: x) -> f(x)

* ##### RETURN/YIELD: A Chained Lambda
ELDAM-LCA 1.0
Life Cycle Assessment data management
Requires Python: >=3.6
Keywords: Life Cycle Assessment,Life Cycle Inventory,Data Quality

ELDAM
=====

.. image:: files/icons/ELDAM.png

**ELDAM (ELsa DAta Manager)** is a program developed by Gustave Coste (`gustave.coste@inra.fr <mailto:gustave.coste@inra.fr>`_) for Life Cycle Analysis data management and conversion based on `ELSA <http://www.elsa-lca.org/>`_'s Quality Management System.

It allows conversion from SimaPro .xlsx export files to Elda spreadsheet and from Elda spreadsheet to SimaPro import csv file.

It features an integrated GUI for graphical use and can also be used easily within a python environment with its API.

.. code-block:: python

    from eldam.core.simapro import SimaProXlsxReader, SimaProCsvWriter
    from eldam.core.elda import Elda

    # SimaPro export file -> Elda spreadsheet
    process = SimaProXlsxReader('path/to/xlsx/file').to_processes()[0]
    elda = Elda()
    elda.add_version(process)
    elda.save('path/to/elda')

    # Elda spreadsheet -> SimaPro import file
    elda = Elda('path/to/elda')
    process = elda.read_last_version().to_process()
    SimaProCsvWriter(process).to_csv('path/to/csv')
  

Features
--------

- Conversion from SimaPro .xlsx export file to Elda spreadsheet
- Conversion from Elda spreadsheet to SimaPro import csv file
- Comparison between SimaPro export file and Elda spreadsheet
- Elda files indexing
- Graphical user interface
- Process detailed view
- Process quality check

Installation and documentation
------------------------------

- User documentation: `framagit.org/GustaveCoste/eldam/wikis/home <https://framagit.org/GustaveCoste/eldam/wikis/home>`_
- API documentation: `eldam-lca.readthedocs.io <https://eldam-lca.readthedocs.io>`_
- Download: `framagit.org/GustaveCoste/eldam/wikis/FAQ/download <https://framagit.org/GustaveCoste/eldam/wikis/FAQ/download>`_

Contribute
----------

- Issue Tracker: `framagit.org/GustaveCoste/eldam/issues <https://framagit.org/GustaveCoste/eldam/issues>`_
- Source Code: `framagit.org/GustaveCoste/eldam <https://framagit.org/GustaveCoste/eldam>`_

Support
-------

If you are having issues, please contact `gustave.coste@inra.fr <mailto:gustave.coste@inra.fr>`_.

Funded by
---------

.. image:: ../../files/icons/eu.png
.. image:: ../../files/icons/eu_occitanie.png
.. image:: ../../files/icons/occitanie.png
DocifyPLUS 1.0.0
A module containing objects for documentating Lambdas, Fucntions, Methods, etc.
Requires Python: >=3.6

# Docify
##### *Doficy is a package for documenting objects*

---

## __Instillation__
    python -m pip install DocifyPLUS

## __Functions__

#### *document*

* ##### PARAMETERS: obj -> Any, doc -> str (doc-string preferably)

* ##### RETURN/YIELD: None
youtubemeta 0.0.2
Download YouTube channel data
Requires Python: >=3.6

# youtubemeta
Download data from YouTube channels - Video titles, view counts, dates, and URLs

To get started  

`pip install youtubemeta`

Then import the module and run for the channel  

```
import youtubemeta  
youtubemeta.scrape('CHANNEL_NAME')
```

Arguments

```
--write    write to CSV
--path     specify custom path and filename for the csv
```