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I have a dataset which contains house prices as well as both continuous features, such as the area, and categorical ones, such as the neighborhood. I would like to build a model which predict house prices (using scikit-learn).

For continuous features there is linear regression and for categorical ones multinomial logistic regression, but what is the recommended way to combine the two?

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What matters is the "type" of your dependent variable, i.e., if it is a price then I guess that it continuous. Thus, any linear regression would do the trick. Such models can easily deal with both categorical and continuous independent variables.

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