May-02-2020, 10:26 AM
Hello to all of you,
I 've created a (fake, for educational reasons only on user profiling), online store. My store tracks the "movements-clicks" of visitors and based on these clicks my system predicts if the user is a man, woman, or if he-she is a parent.
My online store sells athletic accessories. If a user enters the "male" section more often then "probably" he is a man. If the user enters the female section then "probably" she is a woman. The same way, if the user is entering the kids section, then my tracking system is guessing that this user might be a parent. My tracking system also saves info about the sports that my users like, how often they login to my site, how much money they've spent etc. I have records in my database for more than 100 users.
Some of this tracking can be seen in the following table.
[Image: DATA.jpg]
So as an example my tracking system for user with ID 3 predicts that:
man: 0.0000 %
Woman: 100.0000 %
No child.
For ID 17 predicts that:
Man: 21.0526 %
Woman: 78.9474 %
No child.
For ID 31 predicts that:
Man: 0 %
Woman: 0 %
Parent.
For ID 38 predicts that:
Man: 37.5000 %
Woman: 62.5000 %
Parent.
etc
How could I use Python and neural networks to predict any new user coming to my site? The rules that I have to use are:
If
man >= 1 , woman = 0, child = 0 Then he is male, no parent
man >= 1 , woman = 0, child >= 1 Then he is male, parent
man = 0 , woman >=1 , child = 0 Then she is female, no parent
man = 0 , woman >=1 , child >= 1 Then she is female, parent
man = 0 , woman = 0 , child >= 1 parent
If man = woman then nothing can be said, ie if the user have entered 5 times in the male's section and 5 in the female's one, then we cannot predict anything on his/her sex.
Any ideas? Thank you all!
I 've created a (fake, for educational reasons only on user profiling), online store. My store tracks the "movements-clicks" of visitors and based on these clicks my system predicts if the user is a man, woman, or if he-she is a parent.
My online store sells athletic accessories. If a user enters the "male" section more often then "probably" he is a man. If the user enters the female section then "probably" she is a woman. The same way, if the user is entering the kids section, then my tracking system is guessing that this user might be a parent. My tracking system also saves info about the sports that my users like, how often they login to my site, how much money they've spent etc. I have records in my database for more than 100 users.
Some of this tracking can be seen in the following table.
[Image: DATA.jpg]
So as an example my tracking system for user with ID 3 predicts that:
man: 0.0000 %
Woman: 100.0000 %
No child.
For ID 17 predicts that:
Man: 21.0526 %
Woman: 78.9474 %
No child.
For ID 31 predicts that:
Man: 0 %
Woman: 0 %
Parent.
For ID 38 predicts that:
Man: 37.5000 %
Woman: 62.5000 %
Parent.
etc
How could I use Python and neural networks to predict any new user coming to my site? The rules that I have to use are:
If
man >= 1 , woman = 0, child = 0 Then he is male, no parent
man >= 1 , woman = 0, child >= 1 Then he is male, parent
man = 0 , woman >=1 , child = 0 Then she is female, no parent
man = 0 , woman >=1 , child >= 1 Then she is female, parent
man = 0 , woman = 0 , child >= 1 parent
If man = woman then nothing can be said, ie if the user have entered 5 times in the male's section and 5 in the female's one, then we cannot predict anything on his/her sex.
Any ideas? Thank you all!