Clustering Geolocation Data Intelligently in Python

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在此指導項目中,您將:

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中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

visualizationMachine LearningclusteringData Analysismap building

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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.

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

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