Income-predictor | project deals with using Machine Learning
kandi X-RAY | Income-predictor Summary
kandi X-RAY | Income-predictor Summary
Income-predictor is a Jupyter Notebook library. Income-predictor has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.
The project deals with using Machine Learning on Census Income dataset to predict if income is above or below $50K per annum.
The project deals with using Machine Learning on Census Income dataset to predict if income is above or below $50K per annum.
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Quality
Security
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Support
Income-predictor 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.
Income-predictor has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Income-predictor is current.
Quality
Income-predictor has no bugs reported.
Security
Income-predictor has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
Income-predictor 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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Income-predictor releases are not available. You will need to build from source code and install.
Installation instructions, examples and code snippets are available.
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Income-predictor Key Features
No Key Features are available at this moment for Income-predictor.
Income-predictor Examples and Code Snippets
No Code Snippets are available at this moment for Income-predictor.
Community Discussions
No Community Discussions are available at this moment for Income-predictor.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Income-predictor
You can use the requirements.txt file to setup a virtual environment. Whether you are inside a virtual environment or otherwise, you the following command to install all dependencies: pip install -r requirements.txt
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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