Clustering Geolocation Data Intelligently in Python
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
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
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:
An introduction to the problem, as well as basic exploratory data analysis and visualizations
Visualizing geographical data in a more meaningful and interactive way
Methods of evaluating the strength of a clustering algorithm
Theory behind K-Means, and how to use it for our problem
Introduction to density-based clustering approaches, and how to use DBSCAN
Introduction to HDBSCAN, to alleviate constraints of classical DBSCAN
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
Reviews
TOP REVIEWS FROM CLUSTERING GEOLOCATION DATA INTELLIGENTLY IN PYTHON
It would be better if there was no time limit to the project on VM as the VM was too slow.Rest the instructor was awesome ,content was perfect and whatever he taught was very well understood.
It is very interactive and informative course. The instructor is patient in explaining almost every cell of code. I thoroughly enjoyed the course divided in chunks.
Thanks a lot, It was a short, quick and very useful project learning experience. Hope you will provide more geospatial data analysis projects in future soon.
Nice hands-on course to get introduced with the some popular clustering approaches like k-means, DBSCAN, HDBSCAN etc.
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