browser-automationpy
A python project for easily launching custom automated browser to reduce repetitive boring work.
https://github.com/chandraveshchaudhari/browser-automationpy
Science Score: 44.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
○Academic publication links
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○Committers with academic emails
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (13.9%) to scientific vocabulary
Keywords
Repository
A python project for easily launching custom automated browser to reduce repetitive boring work.
Basic Info
- Host: GitHub
- Owner: chandraveshchaudhari
- License: mit
- Language: HTML
- Default Branch: master
- Homepage: https://chandraveshchaudhari.github.io/browser-automationpy/
- Size: 14.9 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Topics
Metadata Files
README.md
An open-source Python framework for Browser Automation : browser-automationpy
Chaudhari, C., Purswani, G. (2023). Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 608. Springer, Singapore. https://doi.org/10.1007/978-981-19-9225-4_17
Introduction
The main objective of the Python framework is easily launching custom automated browser. This framework doesn't ask you to download any browser binaries. Browser automation is very useful in data mining and automation of monotonous work.
Authors
The packages systematic-reviewpy and browser-automationpy are part of Thesis of Chandravesh chaudhari, Doctoral candidate at CHRIST (Deemed to be University), Bangalore, India under supervision of Dr. Geetanjali purswani.
Features
- one line code to install all supported browsers.
- easy customisation for browsers such as adding extensions and changing download paths.
- easy methods for clicking buttons or inputting data into web elements.
- support for chrome, safari, mozilla, edge etc.
- No need to download binary.
Advantages over manual methods
- Saves time
- Automate monotonous tasks
Installation
This project is available at PyPI. For help in
installation check instructions
bash
python3 -m pip install browser-automationpy
Dependencies
Required
- webdriver-manager - Library provides the way to automatically manage drivers for different browsers
- selenium - The selenium package is used to automate web browser interaction from Python. ##### Optional
- PyAutoGUI - PyAutoGUI lets Python control the mouse and keyboard, and other GUI automation tasks.
Important links
Contribution
all kinds of contributions are appreciated. - Improving readability of documentation - Feature Request - Reporting bugs - Contribute code - Asking questions in discussions
Owner
- Name: chandravesh chaudhari
- Login: chandraveshchaudhari
- Kind: user
- Repositories: 23
- Profile: https://github.com/chandraveshchaudhari
Citation (CITATION.bib)
@InProceedings{10.1007/978-981-19-9225-4_17,
author="Chaudhari, Chandravesh
and Purswani, Geetanjali",
editor="Kumar, Sandeep
and Sharma, Harish
and Balachandran, K.
and Kim, Joong Hoon
and Bansal, Jagdish Chand",
title="Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review",
booktitle="Third Congress on Intelligent Systems",
year="2023",
publisher="Springer Nature Singapore",
address="Singapore",
pages="219--233",
abstract="This paper systematically reviews the literature related to stock price prediction systems. The reviewers collected 6222 research works from 12 databases. The reviewers reviewed the full-text of 10 studies in preliminary search and 70 studies selected based on PRISMA. This paper uses the PRISMA-based Python framework systematic-reviewpy to conduct this systematic review and browser-automationpy to automate downloading of full-texts. The programming code with comprehensive documentation, citation data, input variables, and reviews spreadsheets is provided, making this review replicable, open-source, and free from human errors in selecting studies. The reviewed literature is categorized based on type of prediction systems to demonstrate the evolution of techniques and research gaps. The reviewed literature is 7 {\%} statistical, 9{\%} machine learning, 23{\%} deep learning, 20{\%} hybrid, 25{\%} combination of machine learning and deep learning, and 14{\%} studies explore multiple categories of techniques. This review provides detailed information on prediction techniques, competitor techniques, performance metrics, input variables, data timing, and research gap to enable researchers to create prediction systems per technique category. The review showed that stock trading data is most used and collected from Yahoo! Finance. Studies showed that sentiment data improved stock prediction, and most papers used tweets from Twitter. Most of the reviewed studies showed significant improvements in predictions to previous systems.",
isbn="978-981-19-9225-4"
}
GitHub Events
Total
Last Year
Committers
Last synced: almost 3 years ago
All Time
- Total Commits: 24
- Total Committers: 1
- Avg Commits per committer: 24.0
- Development Distribution Score (DDS): 0.0
Top Committers
| Name | Commits | |
|---|---|---|
| chandraveshchaudhari | c****i@g****m | 24 |
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Last synced: 6 months ago
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- Total issues: 0
- Total pull requests: 0
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- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
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Packages
- Total packages: 4
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Total downloads:
- pypi 117 last-month
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Total dependent packages: 0
(may contain duplicates) -
Total dependent repositories: 0
(may contain duplicates) - Total versions: 5
- Total maintainers: 1
pypi.org: browser-automationpy
A python project for easily launching custom automated browser to reduce repetitive boring work.
- Homepage: https://github.com/chandraveshchaudhari/browser-automationpy
- Documentation: https://browser-automationpy.readthedocs.io/
- License: MIT License
-
Latest release: 0.0.2
published almost 3 years ago
Rankings
Maintainers (1)
pypi.org: brain-multiple-modalities-automl
A Python project
- Homepage: https://github.com/chandraveshchaudhari/browser-automationpy
- Documentation: https://brain-multiple-modalities-automl.readthedocs.io/
- License: MIT License
-
Latest release: 0.0.1
published over 1 year ago
Rankings
Maintainers (1)
pypi.org: multi-modal-automl
A Python project
- Homepage: https://github.com/chandraveshchaudhari/browser-automationpy
- Documentation: https://multi-modal-automl.readthedocs.io/
- License: MIT License
-
Latest release: 0.0.1
published over 1 year ago
Rankings
Maintainers (1)
pypi.org: brain-automl
A Python project
- Homepage: https://github.com/chandraveshchaudhari/browser-automationpy
- Documentation: https://brain-automl.readthedocs.io/
- License: MIT License
-
Latest release: 0.0.1
published over 2 years ago
Rankings
Maintainers (1)
Dependencies
- selenium *
- webdriver-manager *
- selenium *
- webdriver-manager *
- selenium *
- webdriver-manager *