I have a data frame
Client_id 1STCUS 20MICRONS 21STCENM 3MINDIA A127 5 0 1 0 A174 0 0 0 3 A177 1 2 0 0 A188 0 0 8 0 A191 8 5 0 0
I am doing market basket analysis based on the dataset similar to the above. Theses are the clients which viewed different stock market scrips and i am trying to build recommendation system for this. For my first trial I came across association rules. In this we can get answer to questions like, People who viewed this also viewed and with how much confidence. However through my research I came across libraries like mlextend and in there we create frequent itemsets with:
frequent_itemsets = apriori(df_matrix, min_support= 0.001, use_colnames=True)
But it requires boolean columns
Client_id 1STCUS 20MICRONS 21STCENM 3MINDIA A127 True False True False A174 False False False True A177 True True False False A188 False False True False A191 True False True False
However by doing this aren't we loosing an important information like number of times scrips viewed by the user.