I have a dataset of around 15 stocks having the following data format :
TimeStamp | F1 | F2 | F3 | ...Fn | Y TimeStamp: Date and Time (masked) Fi : A random feature(numeric) which contains some information about the target Variable Y: A binary 0/1 target variable to predict. The time column is meta data, not a feature. All other columns are features.
The dataset is sufficiently large. I need to make a predictive model for predicting binary values using the random unlabelled numeric (non binary) features( 71 total features) . Can you help me how to proceed with this problem ? Any LSTMS/ neural network based approach for making better results ?