semvec/pypi-packages huggingface dataset:
ml-models 0.1.7
 This project is based upon basic ml-model testing and building a table.
Requires Python: >=3.8,<4.0

pypi url :: [https://pypi.org/project/ml-models/]
This repository is stacked with the multiple ml models looped over certain times, 

The User can install the ml_models and use the project as shown in driver package. 

The data needs to be concise and at last you'll have a table generated over different ml models with different evaluation technique. With this you can directly check the model to use for.
```
from ml_models import *
from ml_models.build_model import build_table

random_forest_classifier= RandomForestClassifier(n_estimators= 10, criterion="entropy")  

# for decision Tree
# Create Decision Tree classifer object
decision_tree = DecisionTreeClassifier()


nn = MLPClassifier(solver='lbfgs', alpha=1e-5,
                     hidden_layer_sizes=(1), random_state=1)



if __name__ == '__main__':
       # reading the csv file
    df = pd.read_csv("data.csv")
    X = df.iloc[:, :-1] # Features
    y = df.iloc[:, -1] # Target variable

    li_df = []
    for i in range(0,10):
        li_df.append(build_table(X,y,[decision_tree,"Decision Tree",False],
                                [nn, "Artificial Neural Network",True]))

    averages = pd.concat([each.stack() for each in li_df],axis=1)\
                .apply(lambda x:x.mean(),axis=1)\
                .unstack()
    print(averages)


```
balticlsc 0.1.5
Baltic LSC module old_scheme
Requires Python: >=3.10

# Computation Module Template Python
Package `balticlsc` is supposed to help with creating modules for the 
BalticLSC system in the python environment.

### Upload new version of the package
```
python setup.py sdist
twine upload dist/balticlsc-{version}.tar.gz
```

### Download the package
You can simply download the package using `PyPI`:
```
python -m pip install balticlsc
```
or clone the repo and use the code directly.
### Build your module
1. Create an implementation of the [ProcessingInterface](balticlsc/computation_module/old_scheme/processing.py)*.
It should handle tokens according to the documentation.
2. Init in your code the BalticLSC api using yours implementation of processing*:
    ```
    from balticlsc.scheme.api import init_baltic_api
    
    app, rest_client = init_baltic_api(Processing)
    ```
3. Build the proper Dockerfile according to the documentation*.  

*Here you got some example modules on which you can base yours:
1. [Face recogniser](examples/face_recogniser)
isddc-ssh 1.1.0
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────────────────────────────────────────────────────────────────────────
Home-page: http://www.gqylpy.com
Author: 竹永康
Author-email: zhuyk4@lenovo.com
License: Lenovo OI
Description: UNKNOWN
Platform: UNKNOWN
Classifier: Environment :: Web Environment
Classifier: Intended Audience :: Developers
Classifier: Operating System :: OS Independent
Classifier: Topic :: Text Processing :: Indexing
Classifier: Topic :: Utilities
Classifier: Topic :: Internet
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
taminsdk 0.4.0
Unoffical Tamin SDK

Python library for Tamin.ir API
=====================================

This is a Python library for the `Tamin.ir
API <https://Tamin.ir>`__. Using this, you can interact
with Tamin.ir from your Python applications. It supports Python
2.7 and Python 3 (3.6+). For more about information about the
tamin.ir API, visit https://Tamin.ir.

Please see `README <https://github.com/mazafard/tamin-sdk>`__ on the
project page to get started.