XGBoost for Regression Predictive Modeling and Time Series Analysis
Learn how to build, evaluate, and deploy predictive models with expert guidance
PDCreated by Joyce Weiner, Partha Pritam Deka
Explore how to use XGBoost for building accurate regression and time series models with practical, hands-on guidance. Move from installing the Python package to deploying real-world predictive solutions. Gain a solid foundation in both the algorithm's theory and its practical applications.
Packt | Dec 2024 | 308 min
What You Will Learn
You will start by understanding the core concepts behind XGBoost, then move on to hands-on exercises using Python and scikit-learn. Through real-world datasets, you will practice building, evaluating, and interpreting models. Each step is designed to help you apply these skills to your own projects and workflows.
Key Features
- Build and evaluate predictive models using XGBoost for regression and time series data
- Apply feature engineering and encoding techniques to improve model accuracy
- Interpret and deploy models confidently using tools like SHAP and LIME
Target Audience
Ideal for data scientists, analysts, and machine learning practitioners with Python experience who want to advance their predictive modeling skills. If you are looking to apply XGBoost to real-world regression or time series problems and deploy robust models, this course is a great fit.
