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.
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What you'll learn
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
Skills you'll practice
Details to know
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About this Guided Project
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:
Understand the Problem Statement
Import Libraries and Datasets
Practice Opportunity #1 [Optional]
Explore Dataset
Perform Data Visualization
Practice Opportunity #2 [Optional]
Prepare the Data for Training
Train the model
Test Trained Model
Visualize Training/Testing Datasets and Trained Model
Practice Opportunity #3 [Optional]
Recommended experience
Basic Python Programming and Math Background
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Instructor
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
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