Dimensionality Reduction with PCA, t-SNE and UMAP you own this product

prerequisites
basics of Julia • intermediate familiarity with scikit-learn and dimensionality reduction algorithms
skills learned
PCA, t-SNE, and UMAP dimensionality reduction techniques • validating and analyzing output of PCA algorithm • calling Python modules from Julia
Łukasz Kraiński and Bogumił Kamiński
1 week · 4-6 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll use the Julia programming language and dimensionality reduction techniques to visualize housing sales data on a scatter plot. This visualization will allow the marketing team to identify links and demand patterns in sales, and is also a useful tool for noise reduction or variance analysis. You’ll use the popular PCA algorithm to visualize the sales dataset with overlaid clustering assignments from k-means and DBSCAN methods, and then expand Julia’s capabilities by calling Python modules using the PyCall.jl package. This extra flexibility will allow you to explore the t-SNE and UMAP algorithms which have excellent results for high-dimensional datasets.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project authors

Bogumil Kaminski
Bogumił Kamiński is Head of the Decision Analysis and Support Unit and Chairman of the Scientific Council for the Discipline of Economics and Finance at SGH Warsaw School of Economics. He also holds a position of adjunct professor at the Data Science Laboratory at Ryerson University and is affiliated with Fields Institute (Computational Methods in Industrial Mathematics Laboratory). In the Julia community, he is the owner of the JuliaData organization and a member of JuliaStats and JuliaLang organizations on GitHub. He also contributes to the community as the top answerer for the [julia] tag on Stack Overflow.
Lukasz Krainski
Łukasz Kraiński is a research assistant at the Decision Analysis and Support Unit at SGH Warsaw School of Economics. He is a certified cloud engineer with expertise in Azure and GCP cloud platforms. You can find him at tech conferences speaking about MLOps and AI (MLinPL 2019, PositivTech 2020, Data Driven Innovation 2020). Łukasz is also an active developer and maintainer of Julia packages (CGE.jl, SmartTransitionSim.jl).

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
  • Intermediate knowledge of scikit-learn and umap-learn Python packages
  • Intermediate knowledge of plotting libraries
  • Basic usage of MultivariateStats.jl package
  • Basics of PyCall.jl and Conda.jl packages
  • Basics of Arrow data format
TECHNIQUES
  • Intermediate usage of dimensionality reduction algorithms
  • Intermediate analysis of PCA dimensionality reduction characteristics
  • Basic scatterplots in 2D and 3D
  • Basics of calling Python from Julia

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