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)

Introduction to R packages – Rmixmod

This R package is for supervised, unsupervised, and semi-supervised classification with MIXture MODelling (interface of MIXMOD software). First, let's look at a dataset called birds:

> library(Rmixmod) 
> data(birds)
> head(birds) gender eyebrow collar sub-caudal border 1 male poor pronounced dotted white few 2 female none dotted black none 3 female pronounced none white none 4 male pronounced dotted white none 5 male pronounced dotted white none 6 male pronounced dotted white none > dim(birds) [1] 69 5

From the previous output, we know that there are 69 observations with 5 characteristics. The following example code is designed to plot bars based on eyebrow and collar:

x <- mixmodCluster(birds,2) 
bb<-barplotCluster...