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.
Cervical Cancer Risk Prediction Using Machine Learning
Instructor: Ryan Ahmed
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What you'll learn
Understand the theory and intuition behind XGBoost Algorithm
Preform exploratory data analysis
Develop, train and evaluate XG-Boost classifier model using Scikit-Learn
Skills you'll practice
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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:
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
Recommended experience
Basic python programming and mathematics.
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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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