Data cleaning, adaptation of missing values, feature selection, correlation analysis, Removal of constant & duplicated values, normalization, outlier removal
Data cleaning, Data handling, Feature selection
Data preprocessing is required to transform raw data into informative data for further usage in modeling, analysis, ... . Overfitting, bias and worse information is avoided with proper preprocessing.
Domain Knowledge & Methodical Knowledge
activity: description what you have to do in your specific level (e.g. define interface)
Data science (analysis & visualisation)
Data Science: Preparation/Integration
GARCÍA, Salvador; LUENGO, Julián; HERRERA, Francisco. Data preprocessing in data mining. Cham, Switzerland: Springer International Publishing, 2015.
In which projects do/did you use this practice?
FDI, COGNIPLANT, SmartDD, DeepRed
3–5 years of experiences
Software Competence Center Hagenberg
|1. How do you rate the potential benefit for your projects?||5|
|2. How often are you using that practice?||5|
|3. What is the effort to introduce the practice in your project upfront?||4|
|4. What is the effort to apply the best practice in your project daily basis?||3|
Questions 1, 3 and 4 (1 = Low, 5 = High)
Question 2 (1 = Never, 5 = Always)