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.
K-Means Clustering 101: World Happiness Report
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4.7
(16 reviews)
Beginner level
Recommended experience
1.5 hours
Learn at your own pace
Hands-on learning
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
Details to know
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Taught in English
No downloads or installation required
Only available on desktop
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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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