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Introduction to Predictive Analytics in Python

In this course you'll learn to use and present logistic regression models for making predictions.

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4 Hours14 Videos52 Exercises13,516 Learners4100 XP

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Course Description

In this course, you will learn how to build a logistic regression model with meaningful variables. You will also learn how to use this model to make predictions and how to present it and its performance to business stakeholders.

  1. 1

    Building Logistic Regression Models


    In this Chapter, you'll learn the basics of logistic regression: how can you predict a binary target with continuous variables and, how should you interpret this model and use it to make predictions for new examples?

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    Introduction and base table structure
    50 xp
    Structure of the base table
    50 xp
    Exploring the base table
    100 xp
    Exploring the predictive variables
    100 xp
    Logistic regression
    50 xp
    Interpretation of coefficients
    50 xp
    Building a logistic regression model
    100 xp
    Showing the coefficients and intercept
    100 xp
    Using the logistic regression model
    50 xp
    Making predictions
    100 xp
    Donor that is most likely to donate
    100 xp
  2. 2

    Forward stepwise variable selection for logistic regression

    In this chapter you'll learn why variable selection is crucial for building a useful model. You'll also learn how to implement forward stepwise variable selection for logistic regression and how to decide on the number of variables to include in your final model.

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Example basetable


loreLore DiricknicksolomonNick Solomonhadrien-d4e73b49-bc29-46b7-a485-2f598f38e3b9Hadrien Lacroix


Intermediate Python
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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