K-Means Clustering 101: World Happiness Report

提供方
Coursera Project Network
在此免费的指导 项目中,您将:

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

在面试中展现此实践经验

Clock1.5 hours
Beginner面向初学者
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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).

必备条件

Basic python programming and mathematics.

您要培养的技能

SegmentationvisualizationMachine LearningPython ProgrammingArtificial Intelligence(AI)

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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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