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CRISP DM Data Mining Process

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Questions and Answers

What is the Cross Industry Standard Process for Data Mining?

A step-by-step process for data mining (correct)

A step-by-step process for data analysis

A step-by-step process for data visualization

A step-by-step process for data engineering

What is important for understanding the objective of the problem, the subject area of the problem, and the data?

Data preparation

Model building

Prior knowledge (correct)

Model evaluation

What is done to prepare the data before modeling?

Data exploration

Data quality checks (correct)

Handling missing values

All of the above

What is done to build and evaluate the model?

Algorithms Signup and view all the answers

What must be created and used to evaluate the model?

Test data Signup and view all the answers

What is repeated for different types of data?

The CRISP DM process Signup and view all the answers

What is important for continuing the CRISP DM process after the model is built?

Training data Signup and view all the answers

What is the first step of the CRISP DM process?

Prior knowledge Signup and view all the answers

What is used to evaluate the model?

Test data Signup and view all the answers

What is done to build and evaluate the model?

Algorithms Signup and view all the answers

Study Notes

The Cross Industry Standard Process for Data Mining, or CRISP DM, is a step-by-step process for data mining.
Prior knowledge is important for understanding the objective of the problem, the subject area of the problem, and the data.
Data must be prepared before modeling can be done. This includes data exploration, data quality checks, handling missing values, data type conversion, transformation, and outliers.
Model building and evaluation is done using algorithms.
Test data must be created and used to evaluate the model.
The CRISP DM process is repeated for different types of data.
Knowledge and actions are important for continuing the CRISP DM process after the model is built. This includes training data, testing the model, and applying the model to new data.

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Description

Test your knowledge of the CRISP DM process for data mining, which involves understanding the problem, preparing the data, building and evaluating models, and applying the models to new data.