Book Image

Hands-On Data Science and Python Machine Learning

By : Frank Kane
Book Image

Hands-On Data Science and Python Machine Learning

By: Frank Kane

Overview of this book

Join Frank Kane, who worked on Amazon and IMDb’s machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank’s successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis.
Table of Contents (11 chapters)

Using mean, median, and mode in Python

Let's start doing some real coding in Python and see how you compute the mean, median, and mode using Python in an IPython Notebook file.

So go ahead and open up the MeanMedianMode.ipynb file from the data files for this section if you'd like to follow along, which I definitely encourage you to do. If you need to go back to that earlier section on where to download these materials from, please go do that, because you will need these files for the section. Let's dive in!

Calculating mean using the NumPy package

What we're going to do is create some fake income data, getting back to our example from the previous section. We're going to create some fake data where...