Data preparation covers a lot of potential ground: data integration, data transformation, feature selection, feature engineering, and much, much more. One of the most basic, and most important, aspects of data preparation is dealing with missing values. Keep in mind that these approaches are only from a technical point of view. There do not in any way address which approach, or combination of approaches, are appropriate in a given scenario. Such decisions depend on an understanding of the data, the domain, and the desired outcome, and cannot be covered in a post such as this.
Sep-14-2017, 15:25:24 GMT