Package Health Score

66 / 100

security
No known security issues
popularity
Small
maintenance
Inactive
community
Active

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Popularity

Small

GitHub Stars: 60.54K
Forks: 25.47K
Contributors: 410

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package mangoml, we found that it has been starred 60,540 times.

Security

No known security issues

Security and license risk for latest version

Release Date: Jun 16, 2018
Direct Vulnerabilities: 0
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0
H
0
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0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
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All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

BSD-3-Clause

Security Policy

Yes

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Maintenance

Inactive

Commit Frequency

Open Issues: 1.56K
Open PR: 559
Last Release: 7 years ago
Last Commit: 3 days ago

Further analysis of the maintenance status of mangoml based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for mangoml is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

As a healthy sign for on-going project maintenance, we found that the GitHub repository had at least 1 pull request or issue interacted with by the community.

Community

Active

Readme: Yes
Contributing.md: Yes
Code of Conduct: Yes
Contributors: 410
Funding: Yes

A good and healthy external contribution signal for mangoml project, which invites more than one hundred open source maintainers to collaborate on the repository.

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Package

Python Versions Compatibility: ==2.*

Age: 7 years
Latest Release: 7 years ago
Dependencies: 13 Direct
Versions: 3
Maintainers: 0
Wheels: OS Independent

Readme

mangoml

mango (MAchine learniNG Orchestra) is a python API for quick machine learning experimentation. The idea is to read data from a CSV file and compare different machine learning models using cross-validation.

Much of the operations used to read the data and test machine learning models are repeated for different experiments and the goal here is to avoid this repetition by implementing wrappers of object from the main python libraries used in data science (e.g. scikit-learn, pandas and xgboost).

Most of the configuration needed is made through a JSON file. The JSON objects are read by wrappers for objects from different APIs. The parameters configured through JSON are the same used by scikit/pandas/xgboost objects so you don't need to code everything again.

I've created this API to make it easier to participate in Kaggle competitions. I used the classic Titanic Competition as an example of mangoml utilization.

Any suggestions can be send to my email: gustavohas@outlook.com

mangoml dependencies

bs4 catboost cython fbprophet lightgbm matplotlib nltk numpy pandas pystan scipy sklearn xgboost

FAQs

What is mangoml?

Simple Machine Learning library. Visit Snyk Advisor to see a full health score report for mangoml, including popularity, security, maintenance & community analysis.

Is mangoml popular?

The python package mangoml receives a total of 37 weekly downloads. As such, mangoml popularity was classified as small. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is mangoml well maintained?

We found indications that mangoml is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is mangoml safe to use?

The python package mangoml was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 24 December-2024, at 11:25 (UTC).

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mangoml

Simple Machine Learning library For more information about how to use this package see README

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Readme

mangoml

mangoml dependencies

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Is mangoml well maintained?

Is mangoml safe to use?

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