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I want to build a model that can detect which driver is driving now the car based on a dataset that contains 20 driver records for 3600s each driver ( the dataset contains all the car sensors values every second ) .

So , now i have that dataset that contain the drivers records . How can i train a model that can identify the driver based on 60 seconds ( or more for example).

Means , i want to make predictions with a dataframe of rows and not a single row . because we can't identify a driver with a single row .

:)

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    $\begingroup$ This is a very interesting problem. First of all I am looking forward to hearing what others have to say. It is a multivariate time series. If we look at it as a supervised learning, should not LSTM-RNN be able to help for multiclass-classification? My question is, how each driver is labeled? An exact driven name/id or a it is rather behavioral labeling? This is very much people at automotive industry are trying to look at. $\endgroup$ Apr 11 '18 at 12:34
  • $\begingroup$ Yes , my first idea was to use LSTM-RNN and i'm looking for how to make data reliable for it ( convert dataframe to time sequences ) . Every driver has it's own ID ( or letter ) as label . $\endgroup$ Apr 11 '18 at 12:42
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    $\begingroup$ Have you seen the answer and suggested links in this post? datascience.stackexchange.com/questions/23196/… I think I pretty much have the very same dataset like yours. I think I wrote a function to sample efficiently (because the sensory data was collected every milliseconds) over the 3-min period; I can share this if you want. But I am not sure this is what you want at this stage. Is not your data already with timestamp? I would actually like to try these methods myself. Happy to collaborate as well if you like. ;-) $\endgroup$ Apr 11 '18 at 12:49
  • $\begingroup$ Sure , it will be great to collaborate with you :) . My data has a timestamp ( every second ) . So , your function must be helpful for me :) . $\endgroup$ Apr 11 '18 at 13:21

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