K-Means Clustering 101: World Happiness Report
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. The World Happiness Report determines the state of global happiness. The happiness scores and rankings data has been collected by asking individuals to rank their life from 0 (worst possible life) to 10 (best possible life).
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- Level Beginner
- Duration 3 hours
- Course by Coursera Project Network
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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. The World Happiness Report determines the state of global happiness. The happiness scores and rankings data has been collected by asking individuals to rank their life from 0 (worst possible life) to 10 (best possible life).Modules
Clustering: World Happiness Report
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- Test Your Understanding
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Labs
- K-Means Clustering 101: World Happiness
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- Reading: Project-based Course Overview
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Explore the fundamentals of K-Means Clustering with a captivating case study on the World Happiness Report. Perfect for beginners in Data Science & AI, this 180-minute Coursera course, led by expert instructors, guides you through clustering countries based on key happiness indicators like economic production and life expectancy. Available for free, this course is ideal for those eager to dive into unsupervised machine learning and global happiness insights.
Ryan Ahmed