WEwesturner·Jan 21, 2019
To add to the three points in the article:
Data quality
https://en.wikipedia.org/wiki/Data_quality
Imputation
https://en.wikipedia.org/wiki/Imputation_(statistics)
Feature selection
https://en.wikipedia.org/wiki/Feature_selection
datacleaner can drop NaNs, do imputation with "the mode (for categorical variables) or median (for continuous variables) on a column-by-column basis", and encode "non-numerical variables (e.g., categorical variables with strings) with numerical equivalents" with Pandas DataFrames and scikit-learn.
https://github.com/rhiever/datacleaner
sklearn-pandas "[maps] DataFrame columns to transformations, which are later recombined into features", and provides "A couple of special transformers that work well with pandas inputs: CategoricalImputer and FunctionTransformer"
https://github.com/scikit-learn-contrib/sklearn-pand
Read the rest
as
Featuretools
https://github.com/Featuretools/featuretools
> Featuretools is a python library for automated feature engineering. [using DFS: Deep Feature Synthesis]
auto-sklearn does feature selection (with e.g. PCA) in a "preprocessing" step; as well as "One-Hot encoding of categorical features, imputation of missing values and the normalization of features or samples"
https://automl.github.io/auto-sklearn/master/manual.html#tur...
auto_ml uses "Deep Learning [with Keras and TensorFlow] to learn features for us, and Gradient Boosting [with XGBoost] to turn those features into accurate predictions"
https://auto-ml.readthedocs.io/en/latest/deep_learning.html#...