The importance of data cleaning

One of the most important initiatives for creating a successful artificial intelligence/machine learning (AI/ML) model is ensuring the data you're using is high quality and clean. That is complete, correct, and relevant to the problem you're trying to solve. Despite the importance of clean data, it can often be overlooked in model creation due to how tedious and time-consuming it can be to review. According to IBM, the lack of clean data, or poor quality data, cost US companies $3.1 trillion in 2016. Accurate models are only built when using clean data.