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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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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  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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  3. 3

    Explaining model performance to business

    Now that you know how to build a good model, you should convince stakeholders to use it by creating appropriate graphs. You will learn how to construct and interpret the cumulative gains curve and lift graph.
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  4. 4

    Interpreting and explaining models

    In a business context, it is often important to explain the intuition behind the model you built. Indeed, if the model and its variables do not make sense, the model might not be used. In this chapter you'll learn how to explain the relationship between the variables in the model and the target by means of predictor insight graphs.
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Lore DirickNick SolomonHadrien 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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