Predicting binary target value based on unlabelled features

by Bibhas Ranjan Das   Last Updated September 05, 2018 14:19 PM

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 ?

Related Questions

How to use ExploreKit

Updated May 27, 2019 01:19 AM