Book Image

Hands-On Data Science with Anaconda

By : Yuxing Yan, James Yan
Book Image

Hands-On Data Science with Anaconda

By: Yuxing Yan, James Yan

Overview of this book

Anaconda is an open source platform that brings together the best tools for data science professionals with more than 100 popular packages supporting Python, Scala, and R languages. Hands-On Data Science with Anaconda gets you started with Anaconda and demonstrates how you can use it to perform data science operations in the real world. The book begins with setting up the environment for Anaconda platform in order to make it accessible for tools and frameworks such as Jupyter, pandas, matplotlib, Python, R, Julia, and more. You’ll walk through package manager Conda, through which you can automatically manage all packages including cross-language dependencies, and work across Linux, macOS, and Windows. You’ll explore all the essentials of data science and linear algebra to perform data science tasks using packages such as SciPy, contrastive, scikit-learn, Rattle, and Rmixmod. Once you’re accustomed to all this, you’ll start with operations in data science such as cleaning, sorting, and data classification. You’ll move on to learning how to perform tasks such as clustering, regression, prediction, and building machine learning models and optimizing them. In addition to this, you’ll learn how to visualize data using the packages available for Julia, Python, and R.
Table of Contents (15 chapters)

Implementation via Octave

The next example of running a linear regression and the related datasets could be downloaded at http://canisius.edu/~yany/data/c9_input.csv. In the following program, the input data set is assumed to be under c:/temp:

a=csvread("c:/temp/c9_input.csv");
x=a(:,2);
y=a(:,3);
figure % open a window for graph
plot(x, y, 'o');
ylabel('Annual returns for S&P500')
xlabel('Annual returns for IBM')

The first graph is shown here:

To save space, the long program will not be shown here. Interested readers can use the previous link. However, its output graph is shown instead:

For the next example, we download an Octave machine library at https://github.com/partharamanujam/octave-ml. Assume that the location of the directory is C:\Octave\octave-ml-master and the related octavelib is C:\Octave\octave-ml-master\octavelib. Then...