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.
Habilidades que você desenvolverá
Aprender passo a passo
Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:
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
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Em um vídeo de tela dividida, seu instrutor te orientará passo a passo
Instrutores
Perguntas Frequentes – FAQ
Perguntas Frequentes – FAQ
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Como funciona a política de reembolso?Como funciona a política de reembolso?
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