Predict Ad Clicks Using Logistic Regression and XG-Boost

Offered By
In this Guided Project, you will:

Train and test an XG-Boost and Logistic Regression models in Scikit-Learn

Perform data cleaning, feature engineering and visualization

Assess the performance of trained classifier models using various KPIs such as accuracy, precision and recall

2 hours
Beginner
No download needed
Split-screen video
English
Desktop only

In this project, we will predict Ads clicks using logistic regression and XG-boost algorithms. In this project, we will assume that you have been hired as a consultant to a start-up that is running a targeted marketing ad campaign on Facebook. The company wants to analyze customer behavior by predicting which customer clicks on the advertisement.

Skills you will develop

  • Python Programming

  • Machine Learning

  • Deep Learning

  • Statistical Classification

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Understand the Problem Statement

  2. Import Libraries and Datasets

  3. Practice Opportunity #1 [Optional]

  4. Explore Dataset

  5. Perform Data Visualization

  6. Practice Opportunity #2 [Optional]

  7. Prepare the Data for Training

  8. Train the model

  9. Test Trained Model

  10. Visualize Training/Testing Datasets and Trained Model

  11. Practice Opportunity #3 [Optional]

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently Asked Questions

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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.

Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.

You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.