K-Means Clustering 101: World Happiness Report

提供方
Coursera Project Network
在此指導項目中,您將:

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
Beginner初級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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).

您要培養的技能

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

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

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