Data cleaning in machine learning (ML) is an indispensable process that significantly influences the accuracy and reliability of predictive models.

But, as a new survey of data scientists and machine learners shows, those expectations need adjusting, because the biggest challenge in these professions is something quite mundane: cleaning dirty ...

By focusing machine learning on systematically getting smarter about how it analyzes, rates and utilizes data, we can not only reduce coding-hours but also worry less about imperfect data.

Poor data quality is enemy number one to the widespread, profitable use of machine learning. While the caustic observation, “garbage-in, garbage-out” has plagued analytics and decision-making ...

Data science work is very often one-off. It is a multi-step process involving data profiling, some data cleansing, continually transforming data from different sources into a single format, saving the ...