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That may be a lot of new binary variables. Your resulting dataset are going to be sparse (numerous zeros). Attribute variety prior may very well be a good suggestion, also try out immediately after.
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To carry out aspect range, we should have Preferably fetched the values from Just about every column on the dataframe to check the independence of each and every feature with the class variable. Could it be a inbuilt operation from my site the sklearn.preprocessing beacuse of which you fetch the values as Just about every row.
It works by using the model precision to identify which attributes (and mixture of attributes) add probably the most to predicting the target attribute.
Just before accomplishing PCA or characteristic selection? In my scenario it is actually taking the function With all the max price as vital attribute.
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