I am completely new to data science and this is a homework assignment so apologies beforehand. I have some raw data on restaurants that contain their categories (e.g. "Pizza", "Italian") and their coordinates. I'm to cluster them based on their coordinates for closeness and their categories for similarity using K-means. So far I've decided to vectorize the data from dictionary format into a NumPy/SciPy representation used by scikit-learn estimators. Something of this format:
['category=Pizza', 'category=Bars', 'cateogry=Italian', 'latitude', 'longitude']
However I'm having trouble scaling the vectors, since spatial coordinates and restaurant categories have different units of scale and there may be 20 different categories for a restaurant and only two features for latitude and longitude. I've attempted to use the skilearn preprocessing library but it is not providing meaningful clusters.
Any help is greatly appreciated.