Nowadays web-sites needs to handle huge amount of traffic. We can
leverage that fact and capture user interactions with the application.
For further analysis.
Next, we can analyze users behavior and capture patterns on which we are able to react properly.
In applications that needs to deal with huge amount of traffic it is
very hard to detect anomalies. We’ll learn how to apply clustering to
find anomalies in web
traffic. Next, we can analyze users behaviour
and when they tend to do on our application using time series data. We
will be using GMM clustering technique to achieve
that.
On the e-commerce sites we want to predict when and what user wants
to buy in the future. We can use the Hidden markov Model to find
transitions between states and
find the transition with highest probability.
About the Author
Tomasz Lelek is a
Software Engineer who programs mostly in Java and Scala. He is a fan of
microservice architectures and functional programming. He dedicates
considerable time and effort to being better every day. Recently, he's
been delving into big data technologies such as Apache Spark and Hadoop.
He is passionate about nearly everything associated with software
development.
Tomasz thinks that we
should always try to consider different solutions and approaches before
solving a problem. Recently, he was a speaker at several conferences in
Poland - Confitura and JDD (Java Developer's Day) and also at Krakow
Scala User Group. You can find the JDD video here:
https://www.youtube.com/watch?v=BnORjQbnZNQ&t - ML Spark talk.
He also conducted a live coding session at Geecon Conference. He is currently working on this website using ML: http://www.allegro.pl
