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
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.
Python Programming
Machine Learning
Deep Learning
Statistical Classification
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]
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
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