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Intermediate Predictive Analytics in Python

Learn how to prepare and organize your data for predictive analytics.

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4 hours15 videos56 exercises
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Course Description

Building good models only succeeds if you have a decent base table to start with. In this course you will learn how to construct a good base table, create variables and prepare your data for modeling. We finish with advanced topics on the matter.
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  1. 1

    Crucial base table concepts

    Free

    In this chapter you will learn how to construct the foundations of your base table, namely the population and the target.

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    The basetable timeline
    50 xp
    Timeline violations
    50 xp
    Available data
    100 xp
    Timeline violation
    100 xp
    The population
    50 xp
    Select the relevant population
    50 xp
    A timeline compliant population
    100 xp
    Removing duplicate objects
    100 xp
    The target
    50 xp
    Calculate an event target
    100 xp
    Calculate an aggregated target
    100 xp
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datasets

Donor IDsBasetable with countries and ageBasetable used in Ex 2.13Living place of donorsDonations

collaborators

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Hadrien Lacroix
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Nick Solomon
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Lore Dirick
Nele Verbiest HeadshotNele Verbiest

Data Scientist at Python Predictions

Nele is a senior data scientist at Python Predictions, after joining in 2014. She holds a master’s degree in mathematical computer science and a PhD in computer science, both from Ghent University. At Python Predictions, she developed several predictive models and recommendation systems in the fields of banking, retail and utilities. Nele has a keen interest in big data technologies and business applications
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