Make A Prediction

Lesson 4 of 8

To wrap up, let’s use the model to make a prediction.

We’re going to invent a fake housing block in San Francisco, in the middle of the Mission district. The block has 2500 rooms, 1000 bedrooms, houses 500 people and 150 households. The apartments are 10 years old on average, and the normalized median income in that neighborhood is 2.0.

For how much could you sell an apartment in that housing block?

We will ask our AI agent to write code that prompts us for all the properties of a single housing block, and then we’ll use the machine learning model to predict what the median house value will be for any apartment in the block.

...

Please provide your access code to unlock and read the remainder of this lesson. Your code will be securely stored in your browser, so you only need to enter it once. If you do not have an access code for this lab, you can get one here.

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
Previous Lesson Next Lesson