Course
Introduction to Predictive Analytics in Python
BasicSkill Level
Updated 11/2022PythonMachine Learning4 hr14 videos52 Exercises4,100 XP22,387Statement of Accomplishment
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Prerequisites
Intermediate Python1
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?
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.
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.
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.
Introduction to Predictive Analytics in Python
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FAQs
What type of model does this course focus on building?
The course centers on logistic regression models for predicting binary outcomes. You learn to select variables, make predictions, and present model results to stakeholders.
Will I learn how to present model results to non-technical audiences?
Yes. Chapter 3 teaches you to construct cumulative gains curves and lift graphs, which are effective tools for demonstrating model value to business stakeholders.
What is forward stepwise variable selection and why does it matter?
It is a method for choosing which variables to include in your model by adding them one at a time. Chapter 2 walks you through implementing this technique for logistic regression.
What Python libraries are needed for this course?
You need Intermediate Python as a prerequisite. The course uses Python for building logistic regression models, performing variable selection, and creating visualizations.
What are predictor insight graphs and when are they useful?
Predictor insight graphs show how each variable in your model relates to the target. Chapter 4 teaches you to create and interpret them to explain your model's logic to others.
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