stock_value_dnn | Stock value regression using DNN on fanancial data
kandi X-RAY | stock_value_dnn Summary
kandi X-RAY | stock_value_dnn Summary
stock_value_dnn is a Python library. stock_value_dnn has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.
Stock value regression using DNN on fanancial data of S&P 500 companies
Stock value regression using DNN on fanancial data of S&P 500 companies
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Quality
Security
License
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Support
stock_value_dnn has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.
It had no major release in the last 6 months.
stock_value_dnn has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of stock_value_dnn is current.
Quality
stock_value_dnn has no bugs reported.
Security
stock_value_dnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
stock_value_dnn 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.
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stock_value_dnn releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
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stock_value_dnn Key Features
No Key Features are available at this moment for stock_value_dnn.
stock_value_dnn Examples and Code Snippets
No Code Snippets are available at this moment for stock_value_dnn.
Community Discussions
No Community Discussions are available at this moment for stock_value_dnn.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install stock_value_dnn
You can download it from GitHub.
You can use stock_value_dnn 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 stock_value_dnn 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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