We have below data with 10 transactions. What is the performance measu

1.	We have below data with 10 transactions. What is the performance measure “Support” for “if white then blue”?
Answer» CRISP-DM stands for Cross Industry Standard Process for DATA Mining. It is a methodology for data science PROGRAMS. It has the following phases: Business understanding – (Typical tasks are: Determine business objective, Assess Situation, Determine Data mining goals, project plan) Data understanding – (Collect initial data, Describe data, Explore Data, Verify Data Quality) Data preparation – (Select data, Clean data, Construct data, Integrate data, FORMAT data) Modelling or Model development – (Select Modelling TECHNIQUES, Generate test design, BUILD model, Assess model) Model evaluation – (Evaluate results, Review process, Determine next steps) Deployment – (Plan deployment, Plan monitoring & maintenance, Product final report & Review Project) Some phases are iterative in nature and any data science project or program which is end to end typically follows this methodology. Below is a diagrammatic view for better understanding

We have below data with 10 transactions. What is the performance measure “Support” for “if white then blue”?

Answer»

CRISP-DM stands for Cross Industry Standard Process for DATA Mining. It is a methodology for data science PROGRAMS. It has the following phases:

Business understanding – (Typical tasks are: Determine business objective, Assess Situation, Determine Data mining goals, project plan)
Data understanding – (Collect initial data, Describe data, Explore Data, Verify Data Quality)
Data preparation – (Select data, Clean data, Construct data, Integrate data, FORMAT data)
Modelling or Model development – (Select Modelling TECHNIQUES, Generate test design, BUILD model, Assess model)
Model evaluation – (Evaluate results, Review process, Determine next steps)
Deployment – (Plan deployment, Plan monitoring & maintenance, Product final report & Review Project)

Some phases are iterative in nature and any data science project or program which is end to end typically follows this methodology.

Below is a diagrammatic view for better understanding