Big_Data_Project | Fake News Detection - Feature Extraction | Machine Learning library

by san089 Python Version: Current License: GPL-3.0

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

kandi X-RAY | Big_Data_Project Summary

Big_Data_Project is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Neural Network applications. Big_Data_Project has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However Big_Data_Project build file is not available. You can download it from GitHub.

In this project we displayed the use machine learning algorithms for text classification. We worked on classifying whether the given news article is fake or real.

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Quality

Security

License

Reuse

Support

Big_Data_Project has a low active ecosystem.

It has 4 star(s) with 3 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

Big_Data_Project has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Big_Data_Project is current.

Quality

Big_Data_Project has no bugs reported.

Security

Big_Data_Project has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Big_Data_Project is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

Big_Data_Project releases are not available. You will need to build from source code and install.

Big_Data_Project has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed Big_Data_Project and discovered the below as its top functions. This is intended to give you an instant insight into Big_Data_Project implemented functionality, and help decide if they suit your requirements.

A passive - agressive checker
Use the hash vectorizer
Use passive aggregation function
Runs the count vectorizer
Analyzes the best predicted model
Compute the classification
Runs the hash vectorizer
Runs the SVM using TFIDF vectorizer
Checks the Naive Bayes test
Generate a count vectorizer
Estimate the class of the model
Run a count vectorizer
Prints the time stats for each vectorizer
Plot classifier times
Plot vectorizer execution time
Clean data
Calculates the voting classifier

Get all kandi verified functions for this library.

Big_Data_Project Key Features

No Key Features are available at this moment for Big_Data_Project.

Big_Data_Project Examples and Code Snippets

No Code Snippets are available at this moment for Big_Data_Project.

Community Discussions

Trending Discussions on Big_Data_Project

How to compare a specific part of one (Ip adress) with other ip adreess in another column in RDD python pyspark without using collect() and for loop

QUESTION

How to compare a specific part of one (Ip adress) with other ip adreess in another column in RDD python pyspark without using collect() and for loop

Asked 2019-Jul-09 at 07:37

I have two lists of Ip addresses which are located in separate txt files. I want to make a comparison between these two data sets by taking the first three bytes of them.

For example:

...

ANSWER

Answered 2019-Jul-09 at 07:37

You just have to generate a key to join on, and then perform the join :

Source https://stackoverflow.com/questions/56938468

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Big_Data_Project

You can download it from GitHub.
You can use Big_Data_Project 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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