#### 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!
Applied Supervised Learning with Python
Preface
Free Chapter
Python Machine Learning Toolkit
Exploratory Data Analysis and Visualization
Regression Analysis
Classification
Ensemble Modeling
Model Evaluation

## Relationships within the Data

There are two reasons why it is important to find relationships between variables in the data:

• Finding which features are potentially important can be deemed essential, since finding ones that have a strong relationship with the target variable will aid in the feature selection process.

• Finding relationships between different features themselves can be useful, since variables in the dataset are usually never completely independent of every other variable and this can affect our modeling in a number of ways.

Now, there are a number of ways we can visualize these relationships, and this really depends on the types of variable we are trying to find the relationship between, and how many we are considering as part of the equation or comparison.

### Relationship between Two Continuous Variables

To find a relationship between two continuous variables is basically to see how one varies as the value of the other is increased. The most common way to visualize this would be using...