Classification with XGBoost

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prerequisites
basics of Julia • basics of data wrangling, hash functions, and visualization techniques
skills learned
comparing distributions of predefined train and test data • building XGBoost model in Julia • evaluating classification model’s quality
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 language to build a classification-based machine learning model that can predict the salary of a customer based on their sociodemographic data. This model will then be used to serve premium advertising to wealthier customers. You’ll build and evaluate XGBoost models with the dedicated Julia XGBoost.jl package, tune the hyperparameters, and assess your model’s capabilities using ROC curve, and measures such as AUC, accuracy, recall, and precision.
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 another high-level programming language such as Python or R
  • Intermediate usage of XGBoost.jl package
  • Basic usage of plotting libraries
  • Basics of Arrow data format and DataFrames.jl
TECHNIQUES
  • Basic data wrangling
  • Basic usage of hash functions
  • Basic visualization techniques (histograms, barplots)
  • Basics of command pipelines
  • Basic serialization
  • Intermediate measuring of feature importance

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