First, I should say I'm beginner in data mining and use Matlab. I have a huge dataset: approximately 40 millions observation. It consists of 24 features including numeric and nominal values like below:
id, click, hour, C1, banner_pos, site_id, site_domain, site_category, app_id, app_domain, app_category, device_id, device_ip, device_model, device_typ,e device_conn_type, C14
one observation is:
1.00E+18, 0, 14102100, 1005, 0, 1fbe01fe, f3845767, 28905ebd, ecad2386, 7801e8d9, 07d7df22, a99f214a, ddd2926e, 44956a24, 1, 2, 15706
All the features and some headers are annonymized.
The problem is to estimate the probability of clicking an advertisement id (click is target feature). What I need is a probabilistic classifier to estimate this that also uses both nominal and numeric features. Lately I used Naive Bayes but it just use numeric and does not give accurate predictions. What is your suggestion?