Book Image

The Python Workshop

By : Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade
Book Image

The Python Workshop

By: Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade

Overview of this book

Have you always wanted to learn Python, but never quite known how to start? More applications than we realize are being developed using Python because it is easy to learn, read, and write. You can now start learning the language quickly and effectively with the help of this interactive tutorial. The Python Workshop starts by showing you how to correctly apply Python syntax to write simple programs, and how to use appropriate Python structures to store and retrieve data. You'll see how to handle files, deal with errors, and use classes and methods to write concise, reusable, and efficient code. As you advance, you'll understand how to use the standard library, debug code to troubleshoot problems, and write unit tests to validate application behavior. You'll gain insights into using the pandas and NumPy libraries for analyzing data, and the graphical libraries of Matplotlib and Seaborn to create impactful data visualizations. By focusing on entry-level data science, you'll build your practical Python skills in a way that mirrors real-world development. Finally, you'll discover the key steps in building and using simple machine learning algorithms. By the end of this Python book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Python.
Table of Contents (13 chapters)

K-Nearest Neighbors, Decision Trees, and Random Forests

Are there other machine learning algorithms, besides LinearRegression(), that is suitable for the Boston Housing dataset? Absolutely. There are many regressors in the scikit-learn library that may be used. Regressors are generally considered a class of machine learning algorithms that are suitable for continuous target values. In addition to Linear Regression, Ridge, and Lasso, we can try K-Nearest Neighbors, Decision Trees, and Random Forests. These models perform well on a wide range of datasets. Let's try them out and analyze them individually.

K-Nearest Neighbors

The idea behind K-Nearest Neighbors (KNN) is straightforward. When choosing the output of a row with an unknown label, the prediction is the same as the output of its k-nearest neighbors, where k may be any whole number.

For instance, let's say that k=3. Given an unknown label, we take n columns for this row and place them in n-dimensional space...