Intermediate Predictive Analytics in Python

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

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4 Hours15 Videos56 Exercises3,475 Learners
4350 XP

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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.

  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

Datasets

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

Collaborators

Lore DirickNick SolomonHadrien Lacroix
Nele Verbiest Headshot

Nele 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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