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 – rattle

Before discussing one or two examples of using rattle, it might be a good idea to discuss an R package called rattle.data. As its name suggests, we could guess that it contains data used by rattle. It is a good idea to use a small dataset to generate a dendrogram. For the next case, we use the first 20 observations from a dataset called wine:

library(rattle.data) 
data(wine)  
x<-head(wine,20) 

To launch rattle, we have the following two lines:

library(rattle) 
rattle() 

For data, we choose R Dataset, then choose x, as shown in the following screenshot. To save space, only the top part is presented here:

The following screenshot shows our choice:

From the previous screenshot, we see 14 observations. Click Clusters, with a default of 10 clusters, and Dendrogram. See the result in the following screenshot:

The previous dendrogram...