We have already covered regular linear regression, as well as linear regression with polynomial terms, and considered training them with both the least squares method and gradient descent. This section of the chapter will consider an additional type of linear regression: multiple linear regression, where more than one type of variable (or feature) is used to construct the model. To examine multiple linear regression, we will use a modified version of the Boston Housing Dataset, available from https://archive.ics.uci.edu/ml/index.php. The modified dataset can be found in the accompanying source code or on GitHub at https://github.com/TrainingByPackt/Supervised-Learning-with-Python and has been reformatted for simplified use. This dataset contains a list of different attributes for property in the Boston area, including the crime rate per capita by town, the percentage of the population with a lower socio-economic status, as well as the average number of rooms per...

#### Applied Supervised Learning with Python

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#### Applied Supervised Learning with Python

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#### Overview of this book

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.
With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn.
This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.
By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Table of Contents (9 chapters)

Applied Supervised Learning with Python

Preface

Free Chapter

Python Machine Learning Toolkit

Exploratory Data Analysis and Visualization

Regression Analysis

Classification

Ensemble Modeling

Model Evaluation

Customer Reviews