Chapter 4. Prepare Data for Modeling
All data is dirty, irrespective of what the source of the data might lead you to believe: it might be your colleague, a telemetry system that monitors your environment, a dataset you download from the web, or some other source. Until you have tested and proven to yourself that your data is in a clean state (we will get to what clean state means in a second), you should neither trust it nor use it for modeling.
Your data can be stained with duplicates, missing observations and outliers, non-existent addresses, wrong phone numbers and area codes, inaccurate geographical coordinates, wrong dates, incorrect labels, mixtures of upper and lower cases, trailing spaces, and many other more subtle problems. It is your job to clean it, irrespective of whether you are a data scientist or data engineer, so you can build a statistical or machine learning model.
Your dataset is considered technically clean if none of the aforementioned problems can be found...
