Classification with XGBoost

This project is part of the Hands-on Data Science with Julia bundle.
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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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 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.

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).


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:

  • 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
  • Basic data wrangling
  • Basic usage of hash functions
  • Basic visualization techniques (histograms, barplots)
  • Basics of command pipelines
  • Basic serialization
  • Intermediate measuring of feature importance

you will learn

In this liveProject, you’ll learn to use Julia to build powerful binary classification models.
  • Comparing distributions of predefined train and test data
  • Building XGBoost model in Julia
  • Evaluating classification model’s quality
  • Analyzing feature importance based on produced model


You choose the schedule and decide how much time to invest as you build your project.
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While within the liveProject platform, get help from other participants and our expert mentors.
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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.