I have a data set with 100,000 instances and about 40 features. Each instance is a customer and each feature is a property of the customer. The first column is binary 0/1 which indices whether the customer click the ads. The task is fitting the data with models or one model and predicting if a new customer will click the ads or not.
I want to start with only one kind of model to do this but don't know which model is suitable. I can think svm (with libsvm), logistic regression. I don't think matrix factorization methods can help because the columns are features of the customer, not items.