In this case study, we will train an unsupervised machine learning algorithm to cluster countries based on features such as economic production, social support, life expectancy, freedom, absence of corruption, and generosity.
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What you'll learn
Understand how to leverage the power of machine learning to perform unsupervised segmentation
Learn how to use Plotly to visualize geographical data
Learn how to obtain the optimal number of clusters using the elbow method
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:
Understand the problem statement and business case
Import datasets and libraries
Perform exploratory data analysis
Perform data visualization - part 1
Perform data visualization - part 1
Prepare the data to feed the clustering model
Understand the intuition behind k-means clustering algorithm
Find the optimal number of clusters
Apply k-means using scikit-learn to perform segmentation
Visualize the clusters
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.
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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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