Spam-Detection | Spam Detection with Linear Support Vector Machine | Machine Learning library

by vineetjoshi253 Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Spam-Detection Summary

Spam-Detection is a Python library typically used in Telecommunications, Media, Advertising, Marketing, Artificial Intelligence, Machine Learning applications. Spam-Detection has no bugs, it has no vulnerabilities and it has low support. However Spam-Detection build file is not available. You can download it from GitHub.

Spam Detection with Linear Support Vector Machine and Multinomial Bayesian Classifier using Sklearn ML library.

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Support

Spam-Detection has a low active ecosystem.

It has 4 star(s) with 0 fork(s). There are no 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 no bugs reported.

Security

Spam-Detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Spam-Detection does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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.

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.

Return a dictionary of all the words in train_path .
Compute the features for each word in the train .
Extract features from constants .
Train the model .

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

h2o Steam Prediction Servlet not accepting character values from python script

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 20:21

The 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.

Source https://stackoverflow.com/questions/44205039

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.

Support

A spam is a irrelevant or unsolicited message sent over the Internet, typically to a large number of users, for the purposes of advertising, phishing, spreading malware, etc. Needless to say, nobody really finds them usefull and thus their detection is quite cruicial. In this project I have built a small spam detector using Support Vactor Machine and Bayseian Classifiers. Given below are steps undertaken to achive the task. Result: The system gave an overall accuracy of 96.18% with a dataset having 702 messages in training set (352 SPAM & 351 Non-Spam) and 260 messages in testing set.

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