Clustering Geolocation Data Intelligently in Python

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在此指导项目中,您将:

Clean and preprocess geolocation data for clustering

Visualize geolocation data interactively using Python

Cluster this data ranging from simple to more advanced methods, and evaluate these clustering algorithms

Clock75-90mins
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

In this 1.5-hour long project, you will learn how to clean and preprocess geolocation data for clustering. You will learn how to export this data into an interactive file that can be better understood for the data. You will learn how to cluster initially with a K-Means approach, before using a more complicated density-based algorithm, DBSCAN. We will discuss how to evaluate these models, and offer improvements to DBSCAN with the introduction of HDBSCAN. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

您要培养的技能

visualizationMachine LearningclusteringData Analysismap building

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. An introduction to the problem, as well as basic exploratory data analysis and visualizations

  2. Visualizing geographical data in a more meaningful and interactive way

  3. Methods of evaluating the strength of a clustering algorithm

  4. Theory behind K-Means, and how to use it for our problem

  5. Introduction to density-based clustering approaches, and how to use DBSCAN

  6. Introduction to HDBSCAN, to alleviate constraints of classical DBSCAN

  7. A simple method to address outliers classified by density-based models.

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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