K-Means Clustering 101: World Happiness Report

ofrecido por
Coursera Project Network
En este proyecto guiado, tú:

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

Clock1.5 hours
BeginnerPrincipiante
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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).

Habilidades que desarrollarás

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

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso

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