  Building accurate and interpretable Machine Learning (ML) models for
heterogeneous/mixed data is a long-standing challenge for algorithms designed
for numeric data. This work focuses on developing numeric coding schemes for
non-numeric attributes for ML algorithms to support accurate and explainable ML
models, methods for lossless visualization of n-D non-numeric categorical data
with visual rule discovery in these visualizations, and accurate and
explainable ML models for categorical data. This study proposes a
classification of mixed data types and analyzes their important role in Machine
Learning. It presents a toolkit for enforcing interpretability of all internal
operations of ML algorithms on mixed data with a visual data exploration on
mixed data. A new Sequential Rule Generation (SRG) algorithm for explainable
rule generation with categorical data is proposed and successfully evaluated in
multiple computational experiments. This work is one of the steps to the full
scope ML algorithms for mixed data supported by lossless visualization of n-D
data in General Line Coordinates beyond Parallel Coordinates.
