Respuesta :
Populating missing data values with default values, correcting data inaccuracies, and summarizing overly detailed data is a feature of the data preparation phase of the CRISP-DM. Hence, option (c) 'data preparation' is the correct answer.
The 'CRoss Industry Standard Process for Data Mining' (CRISP-DM) refers to a process model that functions as the base for a data science process. CRISP-DM is generally a methodology for understanding how business-related problems are solved with data-based solutions. CRISP-DM has six sequential phases:
- Business understanding: Determining the needs of the business need.
- Data understanding: Collecting and analyzing data sets to accomplish the business project, or correct impute.
- Data preparation: Preparing/organizing final datasets by removing erroneous values as well as summarizing data details.
- Modeling: Evaluating various modeling techniques to choose one best.
- Evaluation: Accessing the results of multiple business models and selecting one that best works to meet business objectives.
- Deployment: Delivering the final result or output to the stakeholders.
As per the above discussion, it is obvious that it is the data preparation phase of the CRISP-DM that corrects values with errors, replaces missing values with default data values, and summarizes overly detailed data.
You can leran more about CRISP-DM at
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