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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 Perform accessible machine learning and extreme gradient boosting with Python

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781839218354
Length 310 pages
Edition 1st Edition
Languages
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Author (1):
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Corey Wade Corey Wade
Author Profile Icon Corey Wade
Corey Wade
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Table of Contents (15) Chapters Close

Preface 1. Section 1: Bagging and Boosting
2. Chapter 1: Machine Learning Landscape FREE CHAPTER 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

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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