Saravanan666/pypi-org huggingface dataset:
Package: customer.lascatalinascr_com
customer.lascatalinascr_comCustom Implementations of the propertyshelf MLS embedding for lascatalinascr.comContributorsNote:  place names and roles of the people who contribute to this packagein this file, one to a line, like so:Jens Krause, Original AuthorChangelog1.6add PhotoWall portlet for ListingDetails1.5UX improved AjaxFilterFilter reacts on the choice of “Rental”, “Lot”, “Home”, “Condo”1.4improved filter behaviornew Status Box for Listing Detailsimproved ListingDetail short-info“Back to Results” Linkadd “Clear Filter” link for Filter Portlet1.3Listing Summary templates updated“Status Boxes” indicate now the workflow status of a listing (red for “Sold”)Rentals show now “Price from …” as labelseehttps://propertyshelf.atlassian.net/browse/CUSWEBDEV-2611.2Filter Portlet: Sales&Lot just shows Land Listings [https://propertyshelf.atlassian.net/browse/CUSWEBDEV-259]AjaxSearch: fix pagination bug [https://propertyshelf.atlassian.net/browse/CUSWEBDEV-258]1.1fix test setupclean up code (Flake8)tested for new jQuery (1.8.3) update1.0Confirmed Release 11/19/20140.6RC2deactivate development output, minor clean up0.6RC1AjaxFilter Portlet: Limit bedroom results and checkboxes for form0.5RC1implemented features close to first finalizationAjax based FilterPortletRelated Properties PortletEmbedding Portlet for Listing Detailshighly customizes ListingResults and ListingDetailsmapplic integrationprep overlays, ….1.0devPackage created using zopeskel
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Package: customer-match-translator
A simple command line program to translate a CSV containing customer contact information to Google’s Customer Match data format. Has options to allow for hashing of the data before exporting it to a CSV.InstallationUsing pip:pip install customer-match-translatorExamplecustomer-match-translator contacts.csv -o output.csvWill output a customer match compliant file to output.csv.To hash the file before saving, pass the –hash flag:customer-match-translator --hash contacts.csv -o output.csvSometimes you may not have the zip code of your customers. If you instead have a city or state column, the translator will ask you if you want to try to infer the zipcodes. These cannot be entirely accurate, because there may exist multiple zipcodes for any city.RequirementsThe CSV file must have recognizable field types. Of course, these can simply match the required fields from Google:First NameLast NamePhoneEmailCountryZipIf the field types are not found, the translator will not be able to translate the file.
Package: customer.mlscrcom
Introduction============customer.mlscrcom Installation------------------------------To install customer.mlscrcom into the global Python environment (or a workingenv),using a traditional Zope 2 instance, you can do this:* When you're reading this you have probably already run``easy_install customer.mlscrcom``. Find out how to install setuptools(and EasyInstall) here:http://peak.telecommunity.com/DevCenter/EasyInstall* If you are using Zope 2.9 (not 2.10), get `pythonproducts`_ and install itvia::python setup.py install --home /path/to/instanceinto your Zope instance.* Create a file called ``customer.mlscrcom-configure.zcml`` in the``/path/to/instance/etc/package-includes`` directory.  The fileshould only contain this::<include package="customer.mlscrcom" />.. _pythonproducts: http://plone.org/products/pythonproductsAlternatively, if you are using zc.buildout and the plone.recipe.zope2instancerecipe to manage your project, you can do this:* Add ``customer.mlscrcom`` to the list of eggs to install, e.g.:[buildout]...eggs =...customer.mlscrcom* Tell the plone.recipe.zope2instance recipe to install a ZCML slug:[instance]recipe = plone.recipe.zope2instance...zcml =customer.mlscrcom* Re-run buildout, e.g. with:$ ./bin/buildoutYou can skip the ZCML slug if you are going to explicitly include the packagefrom another package's configure.zcml file.customer.mlscrcom is copyright Thomas MassmannThis program is free software; you can redistribute it and/or modifyit under the terms of the GNU General Public License as published bythe Free Software Foundation; either version 2 of the License, or(at your option) any later version.This program is distributed in the hope that it will be useful,but WITHOUT ANY WARRANTY; without even the implied warranty ofMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See theGNU General Public License for more details.You should have received a copy of the GNU General Public Licensealong with this program; if not, write to the Free SoftwareFoundation, Inc., 59 Temple Place, Suite 330, Boston,MA 02111-1307 USA.Changelog=========1.0dev (unreleased)-------------------- Initial release
Package: customer-retention-toolkit
Setup and ConfigurationFor installing the package, run the following:pipinstallcustomer_retention_toolkitFollow these steps to set up and run the package:Create a new virtual environment:python-mvenvvenvInstall the required packages using the providedrequirements.txt:pipinstall-rrequirements.txt3.For demo purposes, execute the code cells inexample.ipynb.When you get to the API section:Start the API with:pythonrun.pyFor web usage of the API:Visit:http://127.0.0.1:5000For detailed API documentation, go to:http://127.0.0.1:5000/docs(Press Enter).Click onget_info, thentry it out. Input any ID from 1 to 3 and hitexecute.The result will appear in theResponsebody.For comprehensive documentation on the project, including step-by-step guides and detailed explanations, visit ourDocumentation.In the competitive world of subscription services, customer retention is key to sustained business success. High churn rates can significantly impact revenue and growth. This project aims to tackle churn by predicting which customers may leave using advanced analytics and machine learning, based on their interaction history and engagement patterns.
Package: customersatisfactionmetrics
Django Survey AppThis Django application allows you to create and manage surveys including CSAT, NPS, CES, and Generic Surveys. It supports both anonymous and logged-in user responses with user metadata tracking.Setup and Installation1. Install the ApplicationTo install thecustomersatisfactionmetricspackage, run the following command:pipinstallcustomersatisfactionmetrics2. Update Django SettingsAddcustomersatisfactionmetricsto theINSTALLED_APPSlist in your Django project'ssettings.pyfile:INSTALLED_APPS=[# ... other installed apps ..."django.contrib.admin","django.contrib.auth","django.contrib.contenttypes","django.contrib.sessions","django.contrib.messages","django.contrib.staticfiles","customersatisfactionmetrics",# Add this line]3. Run MigrationsApply the necessary migrations to your database with the following command:pythonmanage.pymigrate4. Run the Django ServerStart the Django development server:pythonmanage.pyrunserverAccess the admin panel athttp://localhost:8000/admin/.5. Adding Surveys and QuestionsNavigate to the Django admin site.Use the respective sections to add new surveys and questions.6. URL ConfigurationInclude thecustomersatisfactionmetricsURLs in your project'surls.pyfile:fromdjango.urlsimportpath,includeurlpatterns=[path('',include('customersatisfactionmetrics.urls')),# ... other URL patterns ...]Usage1. Access the SurveyTo view a survey, navigate to the URL pathsurvey/slug/<slug>in your web browser, where<slug>is the slug you assigned to the survey in the admin panel. For example:http://localhost:8000/survey/slug/sample-surveyThis URL will display the survey form for users to fill out and submit.2. Embedding Surveys in TemplatesTo easily embed a survey in your Django templates, you can use theinsert_survey_by_slugtemplate tag. This tag allows you to insert a survey form by its slug directly into a template.First, load the template tag in your template file:{% load survey_tags %}Then, use theinsert_survey_by_slugtag to insert a survey form by specifying its slug:{% insert_survey_by_slug 'your-survey-slug' %}Replace'your-survey-slug'with the actual slug of the survey you want to embed. The survey form will be rendered wherever you include this tag in your template.Example:<!DOCTYPE html><html><head><title>Survey Page</title></head><body><h1>Survey Form</h1>{% load survey_tags %}
    {% insert_survey_by_slug 'sample-survey' %}</body></html>This method provides a flexible way to integrate surveys into various parts of your Django application without the need for additional view logic or URL configurations.FeaturesSupports various survey types: CSAT, NPS, CES, and Generic.Allows both anonymous and logged-in user responses.Tracks user metadata like IP address and user agent.ContributingContributions to this project are welcome. Please fork the repository and submit a pull request.LicenseThis project is licensed underMIT License. See theLICENSEfile in the respective folders for more details.ContactFor any queries or further information, please contact us atdevops@pescheck.nl.Thank you for your interest in our Django Survey Project!
Package: customer-segmentation
#### Purpose of the Package# Customer-Segmentation-for-for-Mall
The goal of the project is to group consumers into clusters using the elbow approach. The project also includes scatter plots to show the relationships between the variables and dataset’s columns. Customer Segmentation is the subdivision of a marketplace into discrete client agencies that proportion comparable characteristics. Customer Segmentation may be a effective method to perceive unhappy client needs. Using the above statistics groups can then outperform the opposition through growing uniquely attractive merchandise and services. K method clustering is one of the maximum famous clustering algorithms and normally the primary component practitioners practice while fixing clustering duties to get an concept of the shape of the dataset. The purpose of K method is to organization statistics factors into wonderful non-overlapping subgroups. One of the important software of K method clustering is segmentation of clients to get a higher information of them which in flip may be used to growth the sales of the company.#### Tools used
K-means clustering
Elbow method
Libraries- Pandas, Matplotlib, Numpy, Seaborn, Skleran libraries#### Getting started
The package can be installed using pip`bash pip install customer_segmentation `### Where can be used
The most common ways companies segment their customer base are:
1) Demographic information such as gender, age, marital and marital status, income, education and occupation;
2) Geographic Information. It depends on the scale of your business. For localized businesses, this information may relate to a specific city or county. For large companies, this may mean the customer’s city, state or even country of residence;
3) Psychographic data such as social class, lifestyle, and personality traits;
4) Behavioral data such as spending and consumption habits, product/service usage and desired benefits.#### Usage
This package can be used for the
-Determine the right price for the product.
-Create individualized marketing initiatives.
-Create the best distribution plan possible.
-Select particular product features for implementation.
-Set new product development activities as a priority.### Data
The data was taken from Kaggle competition datasets
Package: customersegmentationrfm
Customer Segmentation: RFMCustomer segmentation and RFM analysisRecency, frequency, and monetary value (RFM) analysis is a technique used in marketing to determine which customers are the most valuable. RFM is useful for estimating a company's revenue from both existing and potential customers by determining which customers are most likely to make repeat purchases of the company's items.This package does the data processing for the inputted data, conducts RFM Analysis and provides ability to visualize the results and identify the most valueable customer segments.Example UsageI used Online Retail Store data to illustrate functionality of the package.Here is the data I used for further analysisThe package helps to investigate the data, find anomalies and understand its' overall structure and feauture distributions.rfm = CustomerSegmentation(df, customer_id='CustomerID', transaction_date='InvoiceDate', amount='Amount')
rfm.exploratory_analysis(df)With the help of this function we can get descriptive statistics, and important information about the data, with which we are going to work.The package enables to find the RFM scores, segment the customers based on their scores.df_scores = rfm.produce_rfm_dateset(df)
df_scores.head()df_scores = rfm.calculate_rfm_score(df_values)
rfm_table = rfm.find_segments(df_scores)
segment_table = rfm.find_segment_df(rfm.rfm_table)
segment_table.head()We can also plot the distribution of segments, and visually identify the ones with highest number of customersrfm.plot_segment_distribution()rfm.find_customers('Champions')
Package: custom-functions
Median housing value predictionThe housing data can be downloaded fromhttps://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 regressionDecision TreeRandom ForestSteps performedWe 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.Folders available and what's in themartifactsIt stores the temporary data required for the codedatasetsIt contains the raw housing data which is cleaned and used for codesdistIt contains the files neccessary to install packages made from this codedocsIt contains the documentation of this projectlogsIt contains the logs for the basic codesnotebookIt contains all the .pynb filesrcIt contains the codes for the projecttestIt contains the test codes to verify all files and folders are presentHow to run the codeOpen miniconda promptChange directory to project folderType the below line to replicate and use theenvironmentcreatedconda env create --file env.ymlconda activate mle-devType the below line toinstall packagepip install dist/median_housing_value_prediction-0.3-py3-none-any.whlTotestthe installation, follow the below stepsChange directory to test/Execute below codes to check if the packages and files are all availablepytest -vNow change directory to src/To open it with jupyter notebook, just o to project folder and type the following in the promptjupyter notebook
Then copy the url from the prompt into a web browser to use jupyter notebookTo check flow of the code usingmlflow, follow the below stepsOpen a new miniconda prompt and change directory to the project folderActivate the conda environment mle-devNow paste the below script to run the servermlflow server --backend-store-uri mlruns/ --default-artifact-root mlruns/ --host localhost --port 5000After running the command, open your browser and paste the url created on the miniconda prompt. Now you may see the UI of Mlflow.Open a new miniconda prompt and change directory to the scripts folder under project folderActivate the conda environment mle-devRun the main.py file by running the below commandpython main.pyWait for a few minutes, and after the code runs successfully, you can check at the directory described in the prompt for the parameters and metrics (or) you can refresh the website and check the recent run to check for result
Package: custom-google-search
py-custom-google-searchThis library is to ease with the usage of custom Google Search API. This means that you can use this library to programatically perform Google searches with the API, without the need for scraping (which means that it is much lighter).InstallationTo install, runpip install custom-google-searchUsageGetting CX Number and API KeyFollowthis tutorialon how to get your CX Number and API KeyProgrammaticallyfromcustom_google_searchimportsearchsearch("nike logo",key=API_KEY,cx=CX_NUMBER)whereAPI_KEYandCX_NUMBERare the API Key and CX Number respectively.
Package: customgpt-client
CustomGPT SDKUsageFirst, create a client:fromcustomgpt_clientimportCustomGPTCustomGPT.api_key="SuperSecretToken"Now you can access to all of our Models:
Example Request will be like this:Creating a Project synchronously:response=CustomGPT.Project.create(project_name='Test',sitemap_path='https://example.com/test.xml',file_data_retension=False,file=file)project_id=response.data.idOr do the same thing with an async version:response=awaitCustomGPT.Project.acreate(project_name='Test',sitemap_path='https://example.com/test.xml',file_data_retension=False,file=file)project_id=response.data.id
Package: custom-guardrails
NeMo Guardrails is an open-source toolkit for easily adding
programmable guardrails to LLM-based conversational systems.
Guardrails (or "rails" for short) are specific ways of controlling the output of an LLM,
e.g., not talking about politics, responding in a particular way to specific user
requests, following a predefined dialog path, using a particular language style,
extracting structured data, etc.Key Benefits:

- Building Trustworthy, Safe and Secure LLM Conversational Systems: The core value of
using NeMo Guardrails is the ability to write fuzzy rails to steer select conversations.
Developers can choose to define the behavior of their LLM-powered bots on certain
topics and keep their creativity un-encumbered for others!

- Connect models, chains, services, and more via actions: LLMs don't need to solve
all the challenges. NeMo Guardrails provides the ability to connect your codebase or
services to your chatbot seamlessly and securely!
Package: customGUI
A simple package to create your own GUI.To get started and create your own GUI, simply write:from customGUI.form_creator import FormCreator

fc = FormCreator('save.txt')

fc.run()To add a new icon select the name of the icon from the taskbar on the left. Once selected,
the item will appear on the Form (unless the chosen item is a Form). Then any of the icon
settings can be modified, including appearance and behaviour. To modify a setting, a correct
value must be input. In case a value is incorrect, a message will appear at the bottom with
instructions on how to input a correct value. Always press Enter to save a new value, otherwise
it won't be remembered.Each icon must have a unique name, which will be used when using the form later in your
application.To save the Form to be able to use it later, press CTRL+S. It will be saved to the file path
specified when creating an object of the FormCreator class (eg.: 'save.txt').In order to load the form from the save file and use it as a standalone window, the following
needs to be written:from customGUI.form_loader import FormLoader

fl = FormLoader('save.txt')

fl.load()

while True:
    fl.update()You can access items from the Form by calling fl.objects['name of an item']. This will return
the corresponding item (or rather an object of the same class with all parameters copied). You can
then modify all parameters as you wish. For example if the object is of the Textbox class, you
can change thetextparameter, by calling fl.objects['name of the Textbox'].text.Other possibilities of this library shall be explained in the non-existent-so-far Documentation.
Package: customidenticon
CustomIdenticonPython library for generate a variety of identicons.Features3 different types of identiconschange the final image size (size of elements, number of elements)change border sizechange background colorchange the transparency of elementschange the output format (PNG, JPEG, etc.)choice of hashing algorithm (including your own)Installationpip install customidenticonUsageEnd image size = size * block_size + border * 2importcustomidenticonidenticon=customidenticon.create("Test data",# Datatype="pixels",# Type of algorithm (pixels, blocks or layers)format="png",# Output formatsalt="",# salt for more variantsbackground="#f0f0f0",# background colorblock_visibility=140,# transparency of elements in the image (0-255)block_size=30,# size of elements (px)border=25,# border (px)size=5,# number of elementshash_func=None# hash function (auto))Saveimportcustomidenticonidenticon=customidenticon.create("Test data",size=5)# identicon = b"\x89PNG\r\n\x1a\n\x00\x00\x00..."# save to filewithopen("identicon.png","wb")asf:f.write(identicon)# to imageimportiofromPILimportImageimage=Image.open(io.BytesIO(identicon))Hash algorithmUsehashlibfor change algorithmimporthashlibimportcustomidenticonidenticon=customidenticon.create("Test data",hash_func=hashlib.sha3_256)Example custom hash algorithmimportcustomidenticonclassMySuperHashAlgorithm:def__init__(self,*args):passdefhexdigest(self):return"0"*200identicon=customidenticon.create("Test data",hash_func=MySuperHashAlgorithm)Examplesimportcustomidenticon# Create github-like (5x5)identicon1=customidenticon.create("Test")# 200x200px (default for all)# oridenticon1=customidenticon.create("Test",type="pixels")# Create "pixels" type (6x6)# End image size 200x200px (6 * 25 + 25 * 2)identicon2=customidenticon.create("Test",size=6,block_size=25)# Create "layers" type (3 layer)# End image size 200x200px (default size = 3 and block_size = 50)identicon4=customidenticon.create("Test",type="layers")# Create "layers" type (8 layer)# End image size 200x200px (8 * 20 + 20 * 2)identicon5=customidenticon.create("Test",type="layers",size=8,block_size=20,border=20)# Create "blocks" type (3x3) (200x200px)identicon6=customidenticon.create("Test",type="block",block_visibility=100)Result
Package: customjson
customjsonThis tool generates the "master" files that are used for thecustom_updaterand thecustom_component_storeFor thecustom_updaterit generates and update these jsonfiles:custom-cards/informationcustom-components/informationFor thecustom_component_storeit generates and updatesludeeus/data/custom-component-store/V1InstallRequire Python version 3.5.3+python3-mpipinstall-UcustomjsonExamplecustomjson--tokenaaabbbccc111222333--modecomponentCLI optionsparamaliasdescription--token-TAn GitHubaccess_tokenwithrepopermissions.--push-PPush a newrepos.jsonfile to the information repo.--mode-MMust becardorcomponent.--version-VPrint the installed version.Add more resourcesComponentsThe easiest way to add additional resources is to reuse the json file used by'custom_updater'if yo have that.Examples:https://github.com/ludeeus/customjson/blob/f0fd9643a3a5af63c5dc02cd228dfad99796fe3e/customjson/components/custom_updater.py#L7-L16But there are other ways if you look in the/customjson/components/dir you can see different implementations that give more granular control.cardslook in the/customjson/cards/dir to see different implementations