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Predicting CTR with Machine Learning in Python

Learn how to predict click-through rates on ads and implement basic machine learning models in Python so that you can see how to better optimize your ads.

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4 Hours15 Videos57 Exercises2,217 Learners
4700 XP

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Course Description

Have you ever wondered how companies like Facebook and Google are able to serve you surprisingly targeted ads that you occasionally click? Well, behind the scenes, they are running sophisticated machine learning models and using rich user data to predict the click-through rate (CTR) for every user who sees those ads. This course will teach you how to implement basic models in Python so that you can see how to better optimize ads with machine learning. Using real-life ad data you’ll learn how to engineer features, build machine learning models using those features, and evaluate your models in the context of CTR prediction. By the end of this course, you’ll have a strong understanding of how you can apply machine learning to make your ads more effective.

  1. 1

    Introduction to CTR and Basic Techniques

    Free

    Chances are you’re on this page because you clicked a link. In this chapter, you’ll learn why click-through-rates (CTR) are integral to targeted advertising, how to perform basic DataFrame manipulation, and how you can use machine learning models to predict CTR.

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    Introduction to click-through rates
    50 xp
    Beginning steps
    100 xp
    Feature exploration
    100 xp
    First evaluation of data
    50 xp
    Overview of machine learning models
    50 xp
    Logistic regression for breast cancer
    100 xp
    Logistic regression for images
    100 xp
    A second toy model
    100 xp
    CTR prediction using decision trees
    50 xp
    Model implementation
    100 xp
    A first CTR model
    100 xp
    Beyond only accuracy
    100 xp
  2. 2

    Exploratory CTR Data Analysis

    This chapter provides the foundations for exploratory data analysis (EDA). Using sample data you’ll use the pandas library to look at columns and data types, explore missing data, and use hashing to perform feature engineering on categorical features. All of which are important when exploring features for more accurate CTR prediction.

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  3. 3

    Model Applications and Improvements

    It’s time to dive deeper. Find out how you can use measures of model performance including precision and recall to answer real-world questions, such as evaluating ROI on ad spend. You’ll also learn ways to improve upon those evaluation metrics, such as ensemble methods and hyperparameter tuning.

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  4. 4

    Deep Learning

    Profits can be heavily impacted by your campaign’s CTR. In this chapter, you’ll learn how deep learning can be used to reduce that risk. You’ll focus on multi-layer perceptron (MLP) and neural network models, and learn how these can be used to capture the complex relationship between variables to more accurately predict CTR. Lastly, you’ll explore how to apply the basics of hyperparameter tuning and regularization to classification models.

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Datasets

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Collaborators

Maggie MatsuiLis Sulmont
Kevin Huo Headshot

Kevin Huo

Data Scientist

Kevin is a data scientist who graduated from the University of Pennsylvania with a focus on Computer Science within Engineering, and Statistics and Finance within Wharton. His interests include machine learning, investing, startups, and solving hard problems. He was previously at Facebook and is currently in the hedge fund space in New York.
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