Understand the theory and intuition behind XGBoost Algorithm
Preform exploratory data analysis
Develop, train and evaluate XG-Boost classifier model using Scikit-Learn
In this hands-on project, we will build and train an XG-Boost classifier to predict whether a person has a risk of having cervical cancer. Cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. Data has been obtained from 858 patients and include features such as number of pregnancies, smoking habits, Sexually Transmitted Disease (STD), demographics, and historic medical records.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Task #1: Understand the Problem Statement and Business Case
Task #2: Import Libraries and Datasets
Task #3: Perform Exploratory Data Analysis
Task #4: Perform Data Visualization
Task #5: Prepare the data before Model Training
Task #6: Understand the Theory and Intuition Behind XG-Boost
Task #7: Train and Evaluate XG-Boost Algorithm
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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