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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
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).
Segmentation
visualization
Machine Learning
Python Programming
Artificial Intelligence(AI)
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
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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