TableTransforms.jl provides transforms that are commonly used in statistics and machine learning. It was developed to address specific needs in feature engineering and works with general Tables.jl tables.
Project mentors: Júlio Hoffimann
Statistical transforms such as PCA, Z-score, etc, can greatly improve the convergence of various statistical learning models, and are widely used in advanced machine learning pipelines. In this project the mentee will learn how to implement advanced transforms such as PPMT and other transforms for imputation of missing values.
Desired skills: Statistics, Machine Learning
Difficulty level: Medium
Expected duration: 350hrs
Utility transforms such as standardization of column names and other string-based transforms are extremely important for digesting real-world data. In this project the mentee will learn good coding practices and will implement various utility transforms available in other languages (e.g. Janitor package in R, pyjanitor in Python).
Desired skills: Text processing, Regex
Difficulty level: Easy
Expected duration: 175hrs
Address open issues in the package.