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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.

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

Free

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?

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.

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.

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.

Datasets

Example basetable

Collaborators

Lore DirickNick SolomonHadrien Lacroix

Prerequisites

Intermediate Python

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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Lloyds Banking Group

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Louis Maiden
Harvard Business School

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Ronald Bowers
Decision Science Analytics, USAA