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Hands-On Gradient Boosting with XGBoost and scikit-learn

You're reading from Hands-On Gradient Boosting with XGBoost and scikit-learn

Product type Book

Published in Oct 2020

Publisher Packt

ISBN-13 9781839218354

Pages 310 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Corey Wade

Table of Contents (15) Chapters

Preface

1. Section 1: Bagging and Boosting

2. Chapter 1: Machine Learning Landscape

3. Chapter 2: Decision Trees in Depth

4. Chapter 3: Bagging with Random Forests

5. Chapter 4: From Gradient Boosting to XGBoost

6. Section 2: XGBoost

7. Chapter 5: XGBoost Unveiled

8. Chapter 6: XGBoost Hyperparameters

9. Chapter 7: Discovering Exoplanets with XGBoost

10. Section 3: Advanced XGBoost

11. Chapter 8: XGBoost Alternative Base Learners

12. Chapter 9: XGBoost Kaggle Masters

13. Chapter 10: XGBoost Model Deployment

14. Other Books You May Enjoy

Leave a review - let other readers know what you think

Encoding mixed data

Imagine that you are working for an EdTech company and your job is to predict student grades to target services aimed at bridging the tech skills gap. Your first step is to load data that contains student grades into pandas.

Loading data

The Student Performance dataset, provided by your company, may be accessed by loading the student-por.csv file that has been imported for you.

Start by importing pandas and silencing warnings. Then, download the dataset and view the first five rows:

import pandas as pd
import warnings
warnings.filterwarnings('ignore')
df = pd.read_csv('student-por.csv')
df.head()

Here is the expected output:

Figure 10.1 – The Student Performance dataset as is

Figure 10.1 – The Student Performance dataset as is

Welcome to the world of industry, where data does not always appear as expected.

A recommended option is to view the CSV file. This can be done in Jupyter Notebooks by locating the folder for this chapter and clicking...

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Authors (1)

Corey Wade

Corey Wade, M.S. Mathematics, M.F.A. Writing & Consciousness, is the founder and director of Berkeley Coding Academy where he teaches Machine Learning and AI to teens from all over the world. Additionally, Corey chairs the Math Department at Berkeley Independent Study where he has received multiple grants to run after-school coding programs to help bridge the tech skills gap. Additional experiences include teaching Natural Language Processing with Hello World, developing Data Science curricula with Pathstream, and publishing statistics and machine learning models with Towards Data Science, Springboard, and Medium.

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