You are creating an app that predicts the price of a house in California based on its exact location. Your app uses a machine learning model that will be trained on a dataset of houses in California. For each house, the dataset contains the price, the number of rooms, the year when the house was built, and the latitude and longitude.
How should you process the latitude and longitude values?
Choose the correct answer from the options below.
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I'm sorry, your answer is not correct.
Bin the latitude and longitude values is incorrect. Binning produces bin numbers, which are categories that must be one-hot encoded.
One-hot encode the latitude and longitude values is incorrect. Latitude and longitude are not categories, so they cannot be one-hot encoded.
Bin the latitude and longitude values, then one-hot encode the bin numbers is correct
Load the latitude and longitude values without further processing is incorrect. This is not recommended, because some machine learning algorithms prioritize large numbers and might conclude that houses in the top-left corner of the state of California are more important than houses in the bottom-right corner.
Check out my video lesson on geographical data procesing.