Hi
I’d like to predict n variables, indexed on date, instead of one.
So, I have daily sales volume of n products, and multiple categorical and continuous variables, created from date, like day of week etc., values taken from Google trends, weather, etc.
I’d like to build a model to predict all sales volumes in one go.
I did study Rossmann, but the prediction part is still hard for me. So far, I concentrated on data cleaning and building a model. Now, I must learn how to actually use the model.
Hi @jc849
Yes, it works
I get all variables at once.
self.data = (TabularList.from_df(self.df_train_valid, path=’.’,
cat_names=self.cat_vars, cont_names=self.cont_vars,
procs=self.procs)
.split_by_idx(range(len(self.df_train_valid)-self.VALID_SIZE,len(self.df_train_valid)))
.label_from_df(cols=self.dep_vars, label_cls=FloatList)
.add_test(TabularList.from_df(self.test_df))
.databunch(bs=64))
Thanks @tomdraug I followed your suggestion and it worked well.
Quick remark for newbies like me doing multiple regression, I think it’s important to scale your dependent variable before the prediction, otherwise the largest one might dominate everything and you end up optimizing your NN for only one prediction.