Submission: easysklearn (Python)

[hellosakshi]

Submitting Author: Name (@github_handle)

• Lara habashy (@larahabashy)
• Ifeanyi Anene (@IfyAnene7)
• Sakshi Jain (@hellosakshi)
• Zhenrui (Eric) Yu (@yzr1996)

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

• 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.

Scope

• Please indicate which category or categories this package falls under:
  • [ ] Data retrieval
  • [ ] Data extraction
  • [ ] Data munging
  • [ ] Data deposition
  • [ ] Reproducibility
  • [ ] Geospatial
  • [ ] Education
  • [ ] Data visualization*
  • [x] Exploratory data analysis
  • [x] Feature selection
  • [x] Model selection
  • [x] Missing data imputation

* 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

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

• [x] does not violate the Terms of Service of any service it interacts with.
• [ ] has an OSI approved license.
• [x] contains a README with instructions for installing the development version.
• [x] includes documentation with examples for all functions.
• [x] contains a vignette with examples of its essential functions and uses.
• [x] has a test suite.
• [ ] has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.

Publication options

• [ ] Do you wish to automatically submit to the Journal of Open Source Software? If so:

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.

• [x] Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

Code of conduct

• [x] I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

P.S. *Have feedback/comments about our review process? Leave a comment here

Editor and Review Templates

Editor and review templates can be found here

[hellosakshi]

I posted here by mistake

[NickleDave]

Hi @hellosakshi could I ask where you meant to post?

We've had multiple people open and then close issues in the last couple of days.

I'm just trying to figure out if someone might be directing people here by accident?

Thank you

[NickleDave]

Never mind I think I figured out! :)

https://github.com/UBC-MDS/software-review-2021

Happy to provide material so you all can get practice submitting for review

[hellosakshi]

Glad you resolved the mystery:)

[lwasser]

i've noticed this too. is this from Tiffany's course?

[NickleDave]

yes, I think so -- I checked their org page and can see that some of the same packages were added there. Probably just a little bit of a misunderstanding

I mean more than one sounded like I actually wanted them to be submitted