You're reading from XGBoost for Regression Predictive Modeling and Time Series Analysis Learn how to build, evaluate, and deploy predictive models with expert guidance

Product type Paperback

Published in Dec 2024

Publisher Packt

ISBN-13 9781805123057

Length 308 pages

Edition 1st Edition

Concepts

Data Governance

Authors (2):

Joyce Weiner

Partha Pritam Deka

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Table of Contents (19) Chapters

Preface

1. Part 1:Introduction to Machine Learning and XGBoost with Case Studies

2. Chapter 1: An Overview of Machine Learning, Classification, and Regression FREE CHAPTER

3. Chapter 2: XGBoost Quick Start Guide with an Iris Data Case Study

4. Chapter 3: Demystifying the XGBoost Paper

5. Chapter 4: Adding on to the Quick Start – Switching out the Dataset with a Housing Data Case Study

6. Part 2: Practical Applications – Data, Features, and Hyperparameters

7. Chapter 5: Classification and Regression Trees, Ensembles, and Deep Learning Models – What’s Best for Your Data?

8. Chapter 6: Data Cleaning, Imbalanced Data, and Other Data Problems

9. Chapter 7: Feature Engineering

10. Chapter 8: Encoding Techniques for Categorical Features

11. Chapter 9: Using XGBoost for Time Series Forecasting

12. Chapter 10: Model Interpretability, Explainability, and Feature Importance with XGBoost

13. Part 3: Model Evaluation Metrics and Putting Your Model into Production

14. Chapter 11: Metrics for Model Evaluations and Comparisons

15. Chapter 12: Managing a Feature Engineering Pipeline in Training and Inference

16. Chapter 13: Deploying Your XGBoost Model

17. Index

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A review of exploratory data analysis

This chapter uses the house prices dataset. This dataset is a valuable resource for understanding the dynamics of the real estate market. In this section, you’ll review exploratory data analysis (EDA), aiming to unravel the underlying insights within this dataset. To perform this analysis, you’ll use essential Python libraries such as pandas, NumPy, Matplotlib, and Seaborn.

Dataset and data description

You can access the house prices dataset via Kaggle: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data. This is a different dataset than the California house prices dataset you used in Chapter 4. To understand the variables within the dataset, you can refer to the accompanying data description: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data?select=data_description.txt. You’ll be using this dataset in subsequent chapters as well.

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You're reading from XGBoost for Regression Predictive Modeling and Time Series Analysis Learn how to build, evaluate, and deploy predictive models with expert guidance

Table of Contents (19) Chapters

A review of exploratory data analysis

Dataset and data description

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Authors (2)

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