K-means and DBSCAN Clustering

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prerequisites
basics of Julia • intermediate data preprocessing • intermediate k-means clustering • intermediate DBSCAN clustering
skills learned
basic data preparation for clustering methods • clustering data with k-means and DBSCAN algorithms • evaluating and visualizing results
Łukasz Kraiński and Bogumił Kamiński
1 week · 4-6 hours per week · INTERMEDIATE
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In this liveProject, you’ll use the Julia language and clustering algorithms to analyze sales data and determine groups of products with similar demand patterns. Clustering is a well-established unsupervised learning technique that’s commonly used to discover patterns and relations in data. You’ll apply k-means and DBSCAN clustering techniques to housing sales data for a retail startup, leveraging your basic Julia skills into mastery of this machine learning task.
This project is a part of the series Hands-on Data Science with Julia.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

prerequisites

This liveProject is for experienced data scientists and data analysts who are interested in building their skills in Julia. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Basics of Jupyter Notebook
  • Basics of Julia, and intermediate experience in another high-level programming language such as Python or R
TECHNIQUES
  • Intermediate data preprocessing
  • Basic clustering-related visualizations
  • Intermediate knowledge of k-means clustering and DBSCAN clustering
  • Basics of elbow method and silhouettes

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