Intermediate Predictive Analytics in Python

Learn how to prepare and organize your data for predictive analytics.
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Clock4 HoursPlay15 VideosCode56 ExercisesGroup2,785 Learners
Database4350 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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  2. 2

    Creating variables

    You will learn how to add variables to the base table that you can use to predict the target.
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  3. 3

    Data preparation

    Once you derived variables from the raw data, it is time to clean the data and prepare it for modeling. In this Chapter we discuss the steps that need to be taken to make your data modeling-ready.
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  4. 4

    Advanced base table concepts

    In some cases, the target or variables change heavily with the seasons. You will learn how you can deal with seasonality by adding different snapshots to the base table.
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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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Decision Science Analytics, USAA

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