Improving Data Quality: The Foundation for Accurate and Reliable Models

This article delves into the significance of feeding high-quality data into machine learning models and sheds light on several data quality issues that, if left unaddressed, can undermine the integrity of data science projects.

In the realm of machine learning, data quality is of paramount importance. The phrase “garbage in, garbage out” succinctly captures the idea that the output of a machine-learning model is only as good as the quality of the data it is fed.

Algorithms rely on the assumption that the data they receive adheres to certain standards and exhibits desirable properties. However, the reality is that our world, ourselves, and the data we generate are far from perfect, carrying inherent imperfections. Understanding and mitigating these imperfections is crucial for building robust and reliable machine learning models.

Let’s delve into them one by one.

DATA QUALITY ASSUMPTIONS

It is important to differentiate between data and quality data. While the term “big data” has gained prominence in recent years, it does not automatically equate to quality…