Skip to main content
HomePython

Intermediate Predictive Analytics in Python

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

Start Course for Free
4 hours15 videos56 exercises5,492 learnersTrophyStatement of Accomplishment

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.
Group

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies


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.
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.
DataCamp for BusinessFor a bespoke solution book a demo.
  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.

    Play Chapter Now
    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
For Business

Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

datasets

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

collaborators

Collaborator's avatar
Hadrien Lacroix
Collaborator's avatar
Nick Solomon
Collaborator's avatar
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
See More

What do other learners have to say?

FAQs

Join over 15 million learners and start Intermediate Predictive Analytics in Python today!

Create Your Free Account

GoogleLinkedInFacebook

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.