fairywang9 / automated-data-preprocessing

A command-line utility program for automating the trivial, frequently occurring data preparation tasks: missing value interpolation, outlier removal, and encoding categorical variables.

Automated Data Preprocessing

A command-line utility program for automating the trivial, frequently occurring data preparation tasks: missing value interpolation, outlier removal, and encoding categorical variables.

• Identify missing values in the data set and replace them with the sentinel NaN value.
• Interpolate missing values using mean for continuous features, mode for discrete features.
• Remove outliers on the assumption that the distribution of the field values follow a normal distribution.
• Encode categorical features using a one-hot encoding schema.

Getting Started

For a copy of the command-line utility program, simply clone the repository by running:

git clone https://github.com/mdkearns/automated-data-preprocessing

inside of the directory where you would like to store the program.

Prerequisites

This program relies on having the NumPy and Pandas Python packages. You can use pip to install the prerequisites for this program as follows:

pip install -r requirements.txt

Running

You can use the program by running

python make_clean.py [options] path/to/your/data

Running

python make_clean.py --help

has the output

usage: make_clean.py [-h] [-a] [-c] [-i] [-m] [-o] [-v] filePath

The make_clean command line utility program automatically performs common data
preprocessing tasks on your uncleaned data sets.

positional arguments:
  filePath           Path to uncleaned data file

optional arguments:
  -h, --help         show this help message and exit
  -a, --all          all
  -c, --categorical  file contains categorical data
  -i, --interpolate  interpolate missing values
  -m, --missing      file is missing field names
  -o, --outliers     outlier detection and removal
  -v, --version      show program's version number and exit

Versioning

We use SemVer for versioning.

Authors

• Matthew D. Kearns - Initial work - mdkearns

See also the list of contributors who participated in this project.

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgements

Thanks to PurpleBooth for the gist providing a helpful README.md template. If you like the template and would like to use it for your project, it can be found here.