Machine learning is revolutionizing industries across South Africa, enabling businesses to harness the power of data to make informed decisions. However, raw data often contains noise and inconsistencies that can hinder the performance of machine learning models. This is where preprocessing techniques come into play. In this comprehensive guide, we'll explore essential machine learning preprocessing techniques tailored for the South African context, along with practical tips for implementation.
Understanding Data Preprocessing
Data preprocessing involves a series of steps to clean and transform raw data into a suitable format for machine learning algorithms. This is a critical phase as the quality of the data directly impacts the accuracy of your models. Common preprocessing techniques include:
