The Complete Guide to Data Preprocessing (Part 1)

Data preprocessing is the process of cleaning, transforming, and organizing your data set in order to prepare it for data analysis and modeling. It aims to improve the quality, integrity, and reliability of the data, and addresses issues such as missing values, noisy data, outliers, and incompatible data formats.

“Garbage in, garbage out” is a known phrase in data science, which expresses the idea that the quality of the results of a model is determined by the quality of its inputs. The more informative and less noisy your data is, the better…