AI project cycle explain class 9
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Answer:
The machine learning life cycle is the cyclical process that data science projects follow. It defines each step that an organization should follow to take advantage of machine learning and artificial intelligence (AI) to derive practical business value.
Problem identification:
Problem scoping:
The 4Ws Canvas:
Who?: refers to the individuals who are involved in, impacted by, and attempting to remedy the situation.
What?: refers to the specifics of the problem and your understanding of it. What sort of problem is it specifically? Is it easy to understand? How can you tell if there is a problem? What evidence is there that the problem exists? etc. You need to figure out what the problem is exactly right away.
Where?: As these elements are related to the problem, pay attention to the problem's context, situation, or location.
Why?: refers to the need for a solution, the benefits that stakeholders will get from the solution and how those benefits will help stakeholders and society as a whole, as well as what will happen to stakeholders after the problem has been solved.
Data acquisition:
In this example, there are two types of data: primary and secondary
Data exploration:
Modelling:
Evaluation:
Deployment:
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