Closed hellosakshi closed 3 years ago
I posted here by mistake
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
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
Glad you resolved the mystery:)
i've noticed this too. is this from Tiffany's course?
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
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
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 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
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the editor you contacted: NATechnical checks
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