Predictive-Maintenance | Predicting maintenance using IOT and ANN

by kashishmadan Python Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Predictive-Maintenance Summary

Predictive-Maintenance is a Python library typically used in Internet of Things (IoT), Arduino applications. Predictive-Maintenance has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Predictive-Maintenance build file is not available. You can download it from GitHub.

Predicting maintenance using IOT and ANN by sending data via Bluetooth

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Quality

Security

License

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Support

Predictive-Maintenance has a low active ecosystem.

It has 10 star(s) with 2 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

Predictive-Maintenance has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Predictive-Maintenance is current.

Quality

Predictive-Maintenance has no bugs reported.

Security

Predictive-Maintenance has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Predictive-Maintenance is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Predictive-Maintenance releases are not available. You will need to build from source code and install.

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

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Predictive-Maintenance Key Features

No Key Features are available at this moment for Predictive-Maintenance.

Predictive-Maintenance Examples and Code Snippets

No Code Snippets are available at this moment for Predictive-Maintenance.

Community Discussions

Trending Discussions on Predictive-Maintenance

Import error while Executing AWS Predictive Maintenance Using Machine Learning Sample

Understanding indexing of data frame with colon operator

QUESTION

Import error while Executing AWS Predictive Maintenance Using Machine Learning Sample

Asked 2020-Apr-28 at 08:50

We are trying to execute and check what kind of output is provided by Predictive Maintenance Using Machine Learning on AWS sample data. We are referring Predictive Maintenance Using Machine Learning and AWS Guide to launch the sample template provided by the AWS. The template is executed properly and we can see the resources in account. Whenever we run the sagemaker notebook for the given example we are getting the error in CloudWatch logs as follows

...

ANSWER

Answered 2020-Apr-28 at 08:50

A fix for this issue is being deployed to the official solution. In the meantime, you can make the changes described here in your SageMaker environment by following the instructions below:

1) In the notebook, please change the framework_version to 1.6.0.

Source https://stackoverflow.com/questions/61389632

QUESTION

Understanding indexing of data frame with colon operator

Asked 2018-Oct-14 at 16:04

I have a data.frame that is from the predictive maintenance R Notebook example by MS.

Now they show how to subset this data.frame like this (showing some of the first rows and some of the last with just one line of code instad of using head() or tail()):

...

ANSWER

Answered 2018-Oct-14 at 16:04

Thanks to @markus and @Aaron Hayman and @G Grothendieck

The colon operator is evaluated first. So that

Source https://stackoverflow.com/questions/52779124

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

Vulnerabilities

No vulnerabilities reported

Install Predictive-Maintenance

You can download it from GitHub.
You can use Predictive-Maintenance 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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