Spam-Detection | machine learning algorithm ( Neural Network | Machine Learning library
kandi X-RAY | Spam-Detection Summary
kandi X-RAY | Spam-Detection Summary
Spam-Detection is a Python library typically used in Telecommunications, Media, Advertising, Marketing, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. Spam-Detection has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Spam-Detection build file is not available. You can download it from GitHub.
Implementing a machine learning algorithm(Neural Network with logistic activation function) to detect if an email is a spam email or a ham.
Implementing a machine learning algorithm(Neural Network with logistic activation function) to detect if an email is a spam email or a ham.
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Quality
Security
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Spam-Detection has a low active ecosystem.
It has 16 star(s) with 9 fork(s). There are 2 watchers for this library.
It had no major release in the last 6 months.
Spam-Detection has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Spam-Detection is current.
Quality
Spam-Detection has 0 bugs and 0 code smells.
Security
Spam-Detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Spam-Detection code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Spam-Detection is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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Spam-Detection releases are not available. You will need to build from source code and install.
Spam-Detection has no build file. You will be need to create the build yourself to build the component from source.
Spam-Detection saves you 54 person hours of effort in developing the same functionality from scratch.
It has 141 lines of code, 12 functions and 1 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Spam-Detection and discovered the below as its top functions. This is intended to give you an instant insight into Spam-Detection implemented functionality, and help decide if they suit your requirements.
- Run train .
- Initialize the model .
- Update the model .
- Read a csv file .
- Generate a random variates .
- Sigmoid function .
- D sigmoid function .
Get all kandi verified functions for this library.
Spam-Detection Key Features
No Key Features are available at this moment for Spam-Detection.
Spam-Detection Examples and Code Snippets
No Code Snippets are available at this moment for Spam-Detection.
Community Discussions
Trending Discussions on Spam-Detection
QUESTION
h2o Steam Prediction Servlet not accepting character values from python script
Asked 2017-May-26 at 20:21
I am using Steam to attempt to build a prediction service using a python preprocessing script. When python passes the cleaned data to the prediction service in the
...ANSWER
Answered 2017-May-26 at 20:21The prediction service is using EasyPredictModelWrapper and it can only use what the underlying model uses. Here it's not clear what model you use, but most use numerical float values. In the for loop code snippet you can see that the number has to be float.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Spam-Detection
You can download it from GitHub.
You can use Spam-Detection like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use Spam-Detection like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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