Creating a multiple output RNN with keras
I'm trying to use a csv file as my input. The csv file has 100000 rows and 10 columns. I would like the first 7 columns to be the input of the network and the last 3 columns to be the output of the network. First 6 columns are demographic information and the last 4 are the number of products they purchased. I would like to predict the number of purchased items for the next 3 visits, (based off demographics and their first purchase).
Can this be done with keras and is RNN the right choice?
Data in csv format looks like...
age,zip,income,weight,waistsize,necksize,p1#,p2#,p3#,p4#
19,92201,30000,220,39,17,9,1,3,1
33,60173,45000,245,44,15,2,5,3,2
python keras rnn
asked Nov 11 '18 at 23:51
python_nube
175
add a comment |
I'm trying to use a csv file as my input. The csv file has 100000 rows and 10 columns. I would like the first 7 columns to be the input of the network and the last 3 columns to be the output of the network. First 6 columns are demographic information and the last 4 are the number of products they purchased. I would like to predict the number of purchased items for the next 3 visits, (based off demographics and their first purchase).
Can this be done with keras and is RNN the right choice?
Data in csv format looks like...
age,zip,income,weight,waistsize,necksize,p1#,p2#,p3#,p4#
19,92201,30000,220,39,17,9,1,3,1
33,60173,45000,245,44,15,2,5,3,2
python keras rnn
asked Nov 11 '18 at 23:51
python_nube
175
Any special reason for RNN. RNN is suitable for time sequence data. But there is no any temporal pattern in your data. Maybe, you can start with one simple linear regression as a baseline, then go to some complex model, such gradient boost, MLP....
– NormanZhu
Nov 12 '18 at 5:19
No reason for RNN. I can honestly say I have no idea what I am doing. I will give your answer a shot.
– python_nube
Nov 14 '18 at 2:05
add a comment |
I'm trying to use a csv file as my input. The csv file has 100000 rows and 10 columns. I would like the first 7 columns to be the input of the network and the last 3 columns to be the output of the network. First 6 columns are demographic information and the last 4 are the number of products they purchased. I would like to predict the number of purchased items for the next 3 visits, (based off demographics and their first purchase).
Can this be done with keras and is RNN the right choice?
Data in csv format looks like...
age,zip,income,weight,waistsize,necksize,p1#,p2#,p3#,p4#
19,92201,30000,220,39,17,9,1,3,1
33,60173,45000,245,44,15,2,5,3,2
python keras rnn
asked Nov 11 '18 at 23:51
python_nube
175
I'm trying to use a csv file as my input. The csv file has 100000 rows and 10 columns. I would like the first 7 columns to be the input of the network and the last 3 columns to be the output of the network. First 6 columns are demographic information and the last 4 are the number of products they purchased. I would like to predict the number of purchased items for the next 3 visits, (based off demographics and their first purchase).
Can this be done with keras and is RNN the right choice?
Data in csv format looks like...
age,zip,income,weight,waistsize,necksize,p1#,p2#,p3#,p4#
19,92201,30000,220,39,17,9,1,3,1
33,60173,45000,245,44,15,2,5,3,2
python keras rnn
python keras rnn
asked Nov 11 '18 at 23:51
python_nube
175
asked Nov 11 '18 at 23:51
python_nube
175
asked Nov 11 '18 at 23:51
python_nube
175
asked Nov 11 '18 at 23:51
python_nube
175
asked Nov 11 '18 at 23:51
python_nube
175
175
Any special reason for RNN. RNN is suitable for time sequence data. But there is no any temporal pattern in your data. Maybe, you can start with one simple linear regression as a baseline, then go to some complex model, such gradient boost, MLP....
– NormanZhu
Nov 12 '18 at 5:19
No reason for RNN. I can honestly say I have no idea what I am doing. I will give your answer a shot.
– python_nube
Nov 14 '18 at 2:05
add a comment |
Any special reason for RNN. RNN is suitable for time sequence data. But there is no any temporal pattern in your data. Maybe, you can start with one simple linear regression as a baseline, then go to some complex model, such gradient boost, MLP....
– NormanZhu
Nov 12 '18 at 5:19
No reason for RNN. I can honestly say I have no idea what I am doing. I will give your answer a shot.
– python_nube
Nov 14 '18 at 2:05
Any special reason for RNN. RNN is suitable for time sequence data. But there is no any temporal pattern in your data. Maybe, you can start with one simple linear regression as a baseline, then go to some complex model, such gradient boost, MLP....
– NormanZhu
Nov 12 '18 at 5:19
Any special reason for RNN. RNN is suitable for time sequence data. But there is no any temporal pattern in your data. Maybe, you can start with one simple linear regression as a baseline, then go to some complex model, such gradient boost, MLP....
– NormanZhu
Nov 12 '18 at 5:19
No reason for RNN. I can honestly say I have no idea what I am doing. I will give your answer a shot.
– python_nube
Nov 14 '18 at 2:05
No reason for RNN. I can honestly say I have no idea what I am doing. I will give your answer a shot.
– python_nube
Nov 14 '18 at 2:05
add a comment |
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Any special reason for RNN. RNN is suitable for time sequence data. But there is no any temporal pattern in your data. Maybe, you can start with one simple linear regression as a baseline, then go to some complex model, such gradient boost, MLP....
– NormanZhu
Nov 12 '18 at 5:19
No reason for RNN. I can honestly say I have no idea what I am doing. I will give your answer a shot.
– python_nube
Nov 14 '18 at 2:05