Book Image

The Supervised Learning Workshop - Second Edition

By : Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur
Book Image

The Supervised Learning Workshop - Second Edition

By: Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

Overview of this book

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society. Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models. Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks. By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.
Table of Contents (9 chapters)

Relationships within the Data

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

  • Establishing 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 in which 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

Establishing a relationship between two continuous variables is basically seeing how one varies as the value of the other is increased...