First extracting year, month, day, time from the datetime. Then you should decide which variables can be treated as category features such as year/month/day and encode them to boolean form using OneHotEncoder. At last using VectorAssembler to assemble the encoded output vector and the other raw input into the features which can be feed into model trainer.
OneHotEncoder and VectorAssembler are feature transformers provided by Spark ML, you can refer https://spark.apache.org/docs/latest/ml-features.html Thanks Yanbo 2016-01-08 7:52 GMT+08:00 Annabel Melongo <melongo_anna...@yahoo.com.invalid >: > Or he can also transform the whole date into a string > > > On Thursday, January 7, 2016 2:25 PM, Sujit Pal <sujitatgt...@gmail.com> > wrote: > > > Hi Jorge, > > Maybe extract things like dd, mm, day of week, time of day from the > datetime string and use them as features? > > -sujit > > > On Thu, Jan 7, 2016 at 11:09 AM, Jorge Machado < > jorge.w.mach...@hotmail.com> wrote: > > Hello all, > > I'm new to machine learning. I'm trying to predict some electric usage > with a decision Free > The data is : > 2015-12-10-10:00, 1200 > 2015-12-11-10:00, 1150 > > My question is : What is the best way to turn date and time into feature > on my Vector ? > > Something like this : Vector (1200, [2015,12,10,10,10] )? > I could not fine any example with value prediction where features had > dates in it. > > Thanks > > Jorge Machado > > Jorge Machado > jo...@jmachado.me > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: user-unsubscr...@spark.apache.org > For additional commands, e-mail: user-h...@spark.apache.org > > > > >
