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PyData

  • 5 simple steps to create meaningful features from clickstream data (with Pandas)

    | Duration: 30min (Frontal)
    Wohl Center, Hall #2

    Predicting client financial behaviour is usually done by financial data sources - such as client financial history, bank and credit statements, etc. In this talk we will review a different type of data source for risk assessment - features engineered from clickstream data gathered by Mixpanel. We will discuss what kind of features can be generated from clickstream data and how to generate these features using Pandas, what is the benefit of these unconventional features, what are the different correlations of the features with user financial behaviour and how can you apply clickstream data analysis to your problems in general.

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