Clustering Geolocation Data Intelligently in Python

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In this Guided Project, you will:

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
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

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.

Skills you will develop

visualizationMachine LearningclusteringData Analysismap building

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. 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.

How Guided Projects work

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

Instructor

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Frequently Asked Questions

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