Email and IP String preprocessing for classification task I am relatively new to the field of data-science, pardon my novice question. What are the available methods to convert email and ip to vectors for online learning algorithms. The classification aim is to assess fraud/non fraud transactions. As continues explanation: the other relevant fields are categorical and they have been vectorised.  
 A: This is a really interesting question! String vectorization is an area of active research right now, and a there's a ton of interesting approaches out there.
First of all, ip addresses are hierarchical, and can be split by decimals into 4 categorical variables, each with 256 levels (watch out for IPv4 vs IPv6 though)!  In a linear model, you can use the top level ip block directly, perhaps interacted with the 2nd, 3rd, and 4th block depending on how much data you have.  In a tree-based model (e.g. a random forest or GBM), try converting the ip address to an integer and modeling it directly.  A random forest or GBM should be able to identify interesting blocks of the ip range for your model. Most databases have functions to do this conversion, and I know there's a really good R package too.
For email addresses, start by splitting on the @ symbol into address, domain.  Domain is probably useful on it's own as a categorical variable, but you might want to further add a variable for .com vs .edu vs .gov, etc. (The urltools package in R can help you extract top-level domains— someone really needs to write an emailtools package!)  For the address part (the bit before the @ symbol), you could use a character n-gram vectorizer to create a very wide, very sparse matrix which you can then use directly in your model, or can further process using something like SVD to reduce it's dimensionality.  You could also try a word vectorizer, splitting on symbols like ., -, and _.
There's a TON of information in those 2 fields— good luck extracting it!

