Regression Using GLM and DecisionTree

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prerequisites
basics of Julia and plotting libraries • basics of data wrangling
skills learned
build linear regression and random forest models • evaluate models and explore their output • save model to the file for further reuse
Łukasz Kraiński and Bogumił Kamiński
1 week · 6-8 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll use the Julia language to build a regression-based machine learning model that can predict median house value in a neighborhood. You’ll start out with a simple linear regression model to give you a baseline value for quality metrics created with Julia’s package for Generalized Linear Models. You’ll then tune and assess a random forest model, and compare and contrast the two approaches to pick the best results.
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 with another high-level programming language such as Python or R
  • Basics of GLM.jl, DecisionTree.jl, and HypothesisTests.jl packages
  • Basics of plotting libraries
  • Basics of Arrow data format and DataFrames.jl
TECHNIQUES
  • Basics of data wrangling
  • Basics visualization techniques (scatterplots, histograms)
  • Basics of bootstrapping
  • Basics of command pipelines
  • Basic serialization
  • Basic statistical hypothesis testing

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