README.md

In ELToulemonde/dataPreparation: Automated Data Preparation

dataPreparation

Data preparation accounts for about 80% of the work during a data science project. Let's take that number down. dataPreparation will allow you to do most of the painful data preparation for a data science project with a minimum amount of code.

This package is - fast (use data.table and exponential search) - RAM efficient (perform operations by reference and column-wise to avoid copying data) - stable (most exceptions are handled) - verbose (log a lot)

Main preparation steps

Before using any machine learning (ML) algorithm, one need to prepare its data. Preparing a data set for a data science project can be long and tricky. The main steps are the followings:

• Read: load the data set (this package don't treat this point: for csv we recommend data.table::fread)
• Correct: most of the times, there are some mistake after reading, wrong format... one have to correct them
• Transform: creating new features from date, categorical, character... in order to have information usable for a ML algorithm (aka: numeric or categorical)
• Filter: get rid of useless information in order to speed up computation
• Pre model transformation: Specific manipulation for the chosen model (handling NA, discretization, one hot encoding, scaling...)
• Shape: put your data set in a nice shape usable by a ML algorithm

Here are the functions available in this package to tackle those issues:

Correct | Transform | Filter | Pre model manipulation| Shape --------- |----------- |-------- |----------- |------------------------ un_factor | generate_date_diffs | fast_filter_variables | fast_handle_na | shape_set find_and_transform_dates | generate_factor_from_date | which_are_constant | fast_discretization | same_shape find_and_transform_numerics | aggregate_by_key | which_are_in_double | fast_scale | set_as_numeric_matrix set_col_as_character | generate_from_factor | which_are_bijection | | one_hot_encoder set_col_as_numeric | generate_from_character |remove_sd_outlier | | set_col_as_date | fast_round |remove_rare_categorical | | set_col_as_factor | target_encode |remove_percentile_outlier| |

All of those functions are integrated in the full pipeline function prepare_set.

For more details on how it work go check our tutorial.

Getting started: 30 seconds to dataPreparation

Installation

Install the package from CRAN:

install.packages("dataPreparation")

To have the latest features, install the package from github:

library(devtools)
install_github("ELToulemonde/dataPreparation")

Test it

Load a toy data set

library(dataPreparation)
data(messy_adult)
head(messy_adult)

Perform full pipeline function

clean_adult <- prepare_set(messy_adult)
head(clean_adult)

That's it. For all functions, you can check out documentation and/or tutorial vignette.

How to Contribute

dataPreparation has been developed and used by many active community members. Your help is very valuable to make it better for everyone.

• Check out call for contributions to see what can be improved, or open an issue if you want something.
• Contribute to add new usesfull features.
• Contribute to the tests to make it more reliable.
• Contribute to the documents to make it clearer for everyone.
• Contribute to the examples to share your experience with other users.
• Open issue if you met problems during development.

For more details, please refer to CONTRIBUTING.

ELToulemonde/dataPreparation documentation built on July 19, 2023, 11:45 a.m.