Binary classificaiton for weather data if its class 1 or class 0 alert I am working on weather data and it has few features that are independent variables such as severity, severity_id, urgency_id etc ... Based on these values, I would like to classify alerts into class 0 or 1. For example, below is row item from data source

Alert | Severity | Sev_Id | Urg_Id | Event      | Sys_Rec(Target Variable) 
--------------------------------------------------------------------------
dummy | Extreme  |   1    |    1   |   STORM    |      1
dummy | Minor    |   3    |    5   |   RIPTIDE  |      0
dummy | Extreme  |   1    |    1   |   HURRICANE|      1


For severity_id 1 it should be class 1 (Yes) and for others its class 0 (No).The objective is to build a general binary classifier using decision trees. So I started with DTClassifier, but later I realized it could also be done with logistic regression. I am confused which would be a better fit for this kind of data for classification. 

Please advice and give me some starting points.
Which model you choose in the end depends on your data. We cannot really answer this for you. Only practice and trials and errors will actually help you doing this.

For binary classification, you can choose among a very diverse range of models, from Logistic Regression to SVMs to Random Forests to Neural Networks. As a rule of thumb, you can use Occam's Razor => for two models giving the same performance, always choose the simplest one. 

So my advice is: try out a few model, compare their performance on a testing set and select whichever works best for you...