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You might decide that the size of the house is A and the price of the house is B, and have an AI system learn this input to output or A to B mapping. Now, rather than just pricing a house based on the size, you might say, well, let's also collect data on the number of bedrooms of this house. In that case, A can be both of these first two columns, and B can be just the price of the house. So given a table of data, given a data set, it's actually up to you, up to your business use case to decide what is A and what is B. Data is often unique to your business. And this is an example of a data set that a real estate agency might have if they're trying to help price houses. And it's up to you to decide, what is A and what is B, and how to choose these definitions of A and B to make it valuable for your business.
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column is the price of the house. And so, if you're trying to build an AI system or machine learning system to help you set prices for houses, or figure out if a house is priced appropriately. <span>You might decide that the size of the house is A and the price of the house is B, and have an AI system learn this input to output or A to B mapping. Now, rather than just pricing a house based on the size, you might say, well, let's also collect data on the number of bedrooms of this house. In that case, A can be both of these first two columns, and B can be just the price of the house. So given a table of data, given a data set, it's actually up to you, up to your business use case to decide what is A and what is B. Data is often unique to your business. And this is an example of a data set that a real estate agency might have if they're trying to help price houses. And it's up to you to decide, what is A and what is B, and how to choose these definitions of A and B to make it valuable for your business. As another example, if you have a certain budget and you want to decide what is the size of house you can afford, then you might decide that the input A is how much does someone spend,


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