Packt Publishig – Identifying Behaviour Patterns using Machine Learning Techniques

Packt Publishig – Identifying Behaviour Patterns using Machine Learning Techniques
English | Size: 237.00 MB
Category: Tutorial

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.

This course will start with clustering that will help to detect network traffic and analyze users behaviour using time series data using Gaussian Mixture Model. By the end, viewers will be able to predict users behaviour using Hidden Markov Model and understand highest probability.

What You Will Learn
• 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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