Learn to identify behaviour patterns based on the user actions on the web site using ML techniques
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, programming mostly in Java, Scala. Fan of microservices architecture, and functional programming. He dedicates considerable time and effort to be better every day. Recently diving into Big Data technologies such as Apache Spark and Hadoop.
- He is passionate about nearly everything associated with software development. Thinking that we should always try to consider different solutions and approaches before solving a problem. Recently he was a speaker at conferences in Poland - Confitura and JDD (Java Developers Day) and also at Krakow Scala User Group.
- He also conducted live coding session at Geecon Conference.JDD: https://www.youtube.com/watch?v=BnORjQbnZNQ&t - ML Spark talkCurrently working on this website using ML: http://www.allegro.pl
- Software Engineers with professional Experience in Scala and Java
- Understand K-Means Clustering to detect network traffic
- Feature Normalization and Categorical Variables
- Analyzing Time Series data using Clustering
- Verifying and Validation of Model
- Identifying Patterns using in time-series data using GMM
- Explore explanation of Hidden Markov Model Explanation
- Using HMM for defining transitions between states
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