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Regression Using GLM and DecisionTree

This project is part of the Hands-on Data Science with Julia bundle.
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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liveProject This project is part of the Hands-on Data Science with Julia bundle. liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save: $10 (33%)
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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.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project authors

Bogumil Kaminski
Bogumił Kamiński is a 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 on Ryerson University and is affiliated with Fields Institute (Computational Methods in Industrial Mathematics Laboratory). In Julia community, he is owner of JuliaData organization and member of JuliaStats and JuliaLang organizations on GitHub. He also contribute to the community as top answerer for [julia] tag on StackOverflow.
Lukasz Krainski
Lukasz Krainski is a Research Assistant at the Decision Analysis and Support Unit at SGH Warsaw School of Economics. He is 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). Lukasz is also 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

you will learn

In this liveProject, you’ll put Julia into practice to build simple regression machine learning models that are in demand across industries.

  • Build linear regression and random forest models
  • Evaluate models and explore their output
  • Save model to the file for further reuse
  • Transform, manipulate and plot data

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.
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