https://www.coursera.org/learn/competitive-data-science/lecture/mpCps/statistics-and-distance-based-features
Statistics and distance based features: groupby and nearest neighbor methods
Neighbors - for e.g. to predict rental prices, features could be number of schools/hospitals in a radius.
CTR example - ad price, ad position, user_id, page_id - you can use group by on user/page to add new features. Or even the previous history of the user.
Bray curtis metric.
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Matrix factorizations: documents/words - dimensionality reduction.
Statistics and distance based features: groupby and nearest neighbor methods
Neighbors - for e.g. to predict rental prices, features could be number of schools/hospitals in a radius.
CTR example - ad price, ad position, user_id, page_id - you can use group by on user/page to add new features. Or even the previous history of the user.
Bray curtis metric.
-------------------------------------
Matrix factorizations: documents/words - dimensionality reduction.
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