SAP HANA Academy – PAL: 143. Classification – SVM Outlier Detection Predict [2.0 SPS 01]

In this video tutorial, Philip Mugglestone shows how to predict potential outliers using a one-class Support Vector Machine (SVM) model via the predictive analysis library with HANA 2.0 SPS 01.

One-class SVM is an unsupervised algorithm that learns a decision function for outlier detection: classifying new data as similar or different to the training set. In one-class SVM scenario, f(∙) refers to decision function, and there is no TARGET needed since it is unsupervised.

To access the code snippets used in the video series please visit


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Data Preprocessing & Exploratory Data Analysis

Please find the jupyter/colab notebook files in the blog slides here:

This is the 5th session of the bootcamp, the session is designed for people who already have some knowledge of Data Preprocessing & Exploratory Data Analysis.

In the session, the speaker talks about what is Data Preprocessing & Exploratory Data Analysis. In addition to why do we need them & what are their uses, the speaker also talks about different techniques.

The session speaker is Ayon Roy. Ayon is a passionate Data Scientist, a speaker, & a problem solver. He has had multiple stints in field of Data Science through various internships. Ayon is currently pursuing engineering in Computer Science from Guru Gobind Singh Indraprastha University.

We encourage you to ask all your doubts. We’ll be responding to each & every query 🙂
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Anomaly Innovation: Platforms, Results, Functions

Anomaly detection is important for data cleaning, cybersecurity, and robust AI systems. This talk will review recent work in our group on (a) benchmarking existing algorithms, (b) developing a theoretical understanding of their behavior, (c) explaining anomaly „alarms“ to a data analyst, and (d) interactively re-ranking candidate anomalies in response to analyst feedback. Then the talk will describe two applications: (a) detecting and diagnosing sensor failures in weather networks and (b) open category detection in supervised learning.

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Outliers – How to detect the outliers and reduce the effect using variable transformation like using log, square root, cube root or other suitable method.
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