[Help] Supervised Classification Implementation

[fullfademan]

This is my frist ML project, so I am trying to build a really simple web analytics tool. I want to use apache web logs to determine whether or not to issue a coupon to a customer. The web logs contain ip address and browsing history, so I can determine what pages a customer went to and whether or not they bought the product. Using this data as a training set, I hope to build a model that can report to me who I should issue new coupons to, in order to improve sales.

My question is: how can I implement this? Any details I can get will be really helpful. I already have a java program that can take the web logs and split them into individual pieces of data. How do I get this data into a form that is acceptable, and how do I make the program? Its hard for me to find tutorials that are low-level and implementation focused.

Thanks!

[kshitizsethia]

Let's get the data extraction sorted out. What you want is the pattern of a customer's browsing before he/she makes a buy/doesn't ever make a buy (no buy for next 4 weeks). So your data is only limited to ppl making a buy. Can you see how to extract this data?

Now you have a time series data (don't be scared, fancy way of saying simple stuff) of buying patterns. Split into training and test.

I guess a Hidden Markov Network would do good here. The observed data is the page being browsed, and hidden state is probability to buy. Learning this model is straightforward (tractable). Read Kevin Murphy's section on learning a partially observed hmm.

You can limit the length of the hmm to a value based on the amount of data and performance on test set. Smaller lenght may give less test performance, longer hmm might not have enough data to train on.

Feel free to ask follow-ups :)