Version submitted:
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD
Description
Include a brief paragraph describing what your package does:
Easysklearn is a python package designed to perform exploratory data analysis, to help with missing data imputation and to give baseline models. Also, it assists in feature selection which is a common problem when undertaking a data science or machine learning analysis.
* Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see notes on categories of our guidebook.
Explain how the and why the package falls under these categories (briefly, 1-2 sentences):
It explores data and impute missing values. Also, it helps in feature selection and helps in selecting the model.
Who is the target audience and what are scientific applications of this package?
The target audience of this package includes anyone who has the requirements to clean data and build machine learning model. For instance, students interested in machine learning might be the target audience. Also, data scientists, data engineers, statisticians can be possible target users.
Are there other Python packages that accomplish the same thing? If so, how does yours differ?
There are some Python packages to explore data (EDA) such as "Pandas Profiling". However, our Python package provides a different working flow and functionality. There are four main components of our Python package and each of the functions has its innovation points. For example, our eda function gives users a sense of the whole distribution summary of the raw data. In addition to EDA, our model helps in filling the missing values, in feature selection and in selecting the best model.
If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:
NA
Technical checks
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JOSS Checks
- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements][JossSubmissionRequirements]. Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS.
- [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements][JossSubmissionRequirements]: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria.
- [ ] The package contains a `paper.md` matching [JOSS's requirements][JossPaperRequirements] with a high-level description in the package root or in `inst/`.
- [ ] The package is deposited in a long-term repository with the DOI:
*Note: Do not submit your package separately to JOSS*
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Submitting Author: Name (@github_handle)
Package Name: easysklearn
One-Line Description of Package: A python package to explore data, to select features, to impute missing data and to select the model
Repository Link: https://github.com/UBC-MDS/524_easysklearn
Version submitted:
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD
Description
Easysklearnis a python package designed to perform exploratory data analysis, to help with missing data imputation and to give baseline models. Also, it assists in feature selection which is a common problem when undertaking a data science or machine learning analysis.Scope
* Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see notes on categories of our guidebook.
Explain how the and why the package falls under these categories (briefly, 1-2 sentences): It explores data and impute missing values. Also, it helps in feature selection and helps in selecting the model.
Who is the target audience and what are scientific applications of this package?
The target audience of this package includes anyone who has the requirements to clean data and build machine learning model. For instance, students interested in machine learning might be the target audience. Also, data scientists, data engineers, statisticians can be possible target users.
Are there other Python packages that accomplish the same thing? If so, how does yours differ? There are some Python packages to explore data (EDA) such as "Pandas Profiling". However, our Python package provides a different working flow and functionality. There are four main components of our Python package and each of the functions has its innovation points. For example, our eda function gives users a sense of the whole distribution summary of the raw data. In addition to EDA, our model helps in filling the missing values, in feature selection and in selecting the best model.
If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or
@tagthe editor you contacted: NATechnical checks
For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:
Publication options
JOSS Checks
- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements][JossSubmissionRequirements]. Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS. - [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements][JossSubmissionRequirements]: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [ ] The package contains a `paper.md` matching [JOSS's requirements][JossPaperRequirements] with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: *Note: Do not submit your package separately to JOSS*Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?
This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.
Code of conduct
P.S. *Have feedback/comments about our review process? Leave a comment here
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