Submission: prepropy (Python)

Submitting Authors: Bruhat Musunuru (BruhatM) , Pan Fan(pan1fan2), Chun Chieh(Jason) Chang (jachang0628)

Package Name: prepropy One-Line Description of Package: Data preprocessing package for machine learning Repository Link: https://github.com/UBC-MDS/prepropy/tree/0.1.7 Version submitted: 0.1.7 Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
Version accepted: TBD

Description

Data preprocessing and EDA are essential to any data science project. EDA provides insights into a dataset , visualizes and interprets the information that is hidden in the dataset. Data preprocessing is crucial to get scale features to train better models and handle missing values. In the real world, datasets contain a large number of features and observations and it is unrealistic to expect that raw dataset is perfect and ready for model building. The package aims to facilitate users to perform data imputation, feature scaling and basic exploratory data analysis for machine learning modeling.

Scope

Please indicate which category or categories this package falls under:
- [ ] Data retrieval
- [ ] Data extraction
- [x] Data munging
- [ ] Data deposition
- [ ] Reproducibility
- [ ] Geospatial
- [ ] Education
- [x] Data visualization*

* 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): Our package contains functions to impute missing values and scale features. These most likely fall under Data munging. We also have a function to Visualize selected features in a dataframe which falls under Data visualization.
Who is the target audience and what are scientific applications of this package?
The target audience for our package is a beginner user who is trying out ML and wants to simplify the pre-processing before implementing ML models.
Are there other Python packages that accomplish the same thing? If so, how does yours differ? There are packages from Sklearn which essentially have the same functionality but our package simplifies the process for simplicity and beginner friendliness.
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:

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[x] includes documentation with examples for all functions.
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Final approval (post-review)

[X] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 2 hours (approx)

Review Comments

Thank you for submitting your package for review. The overall structure looks good and I do not see any major issue. I did notice some minor points which you might be interested in fixing during future enhancements:

The README overview section clearly mentions what users will be able to perform with this package. However, it will be great if you can add a line for target audience.
The usage information of eda function in README works fine, but the example here is not working.
The usage information about impute function in README works after the following changes:
- import numpy as np
- Fixing typo in import prepropy.imputation import imputation
- The usage information about scaler function in README works after the following changes:
- Add import statement from prepropy.scaler import scaler
The tests do no cover one branch of imputation.py and scaler.py. You may want to address this in future releases.