K-Means Clustering 101: World Happiness Report

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In this Guided Project, you will:

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

1.5 hours
Beginner
No download needed
Split-screen video
English
Desktop only

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).

Skills you will develop

  • Segmentation

  • visualization

  • Machine Learning

  • Python Programming

  • Artificial Intelligence(AI)

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:

  1. Understand the problem statement and business case

  2. Import datasets and libraries

  3. Perform exploratory data analysis

  4. Perform data visualization - part 1

  5. Perform data visualization - part 1

  6. Prepare the data to feed the clustering model

  7. Understand the intuition behind k-means clustering algorithm

  8. Find the optimal number of clusters

  9. Apply k-means using scikit-learn to perform segmentation

  10. Visualize the clusters

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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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