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 Python packages – sklearn (scikit-learn)

First, let's look at the functions contained in the Python package called sklearn. The code has just three lines:

import sklearn as sk 
x=dir(sk) 
print(x) 

The related output is shown here:

For one specific submodule, it is called sklearn.cluster, as shown:

from sklearn import cluster 
x=dir(cluster) 
print(x) 

The output is shown here:

In addition, we can show many embedded datasets by using the following three lines of Python code:

import sklearn.datasets as datasets 
x=dir(datasets) 
print(x) 

The output is shown here:

For example, if we want to use a dataset called iris, we can apply the load_iris() function, as shown:

from sklearn import datasets 
data= datasets.load_iris() 
print(data) 

The first few lines are shown here:

The following code is one example of using the module:

from sklearn import cluster...