Book Image

Learning Predictive Analytics with Python

By : Ashish Kumar, Gary Dougan
Book Image

Learning Predictive Analytics with Python

By: Ashish Kumar, Gary Dougan

Overview of this book

Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form - It needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Learning to predict who would win, lose, buy, lie, or die with Python is an indispensable skill set to have in this data age. This book is your guide to getting started with Predictive Analytics using Python. You will see how to process data and make predictive models from it. We balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and numpy. You’ll start by getting an understanding of the basics of predictive modeling, then you will see how to cleanse your data of impurities and get it ready it for predictive modeling. You will also learn more about the best predictive modeling algorithms such as Linear Regression, Decision Trees, and Logistic Regression. Finally, you will see the best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world.

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Learning Predictive Analytics with Python
Ashish Kumar | Gary Dougan
Feb, 2016 | 354 pages
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Table of Contents (19 chapters)
Learning Predictive Analytics with Python
Learning Predictive Analytics with Python
Credits
Credits
Foreword
Foreword
About the Author
About the Author
Acknowledgments
Acknowledgments
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Getting Started with Predictive Modelling
Getting Started with Predictive Modelling
Introducing predictive modelling
Applications and examples of predictive modelling
Python and its packages – download and installation
Python and its packages for predictive modelling
IDEs for Python
Summary
2
Data Cleaning
Data Cleaning
Reading the data – variations and examples
Various methods of importing data in Python
The read_csv method
Use cases of the read_csv method
Case 2 – reading a dataset using the open method of Python
Case 3 – reading data from a URL
Case 4 – miscellaneous cases
Basics – summary, dimensions, and structure
Handling missing values
Creating dummy variables
Visualizing a dataset by basic plotting
Summary
3
Data Wrangling
Data Wrangling
Subsetting a dataset
Generating random numbers and their usage
Grouping the data – aggregation, filtering, and transformation
Random sampling – splitting a dataset in training and testing datasets
Concatenating and appending data
Merging/joining datasets
Summary
4
Statistical Concepts for Predictive Modelling
Statistical Concepts for Predictive Modelling
Random sampling and the central limit theorem
Hypothesis testing
Chi-square tests
Correlation
Summary
5
Linear Regression with Python
Linear Regression with Python
Understanding the maths behind linear regression
Making sense of result parameters
Implementing linear regression with Python
Model validation
Handling other issues in linear regression
Summary
6
Logistic Regression with Python
Logistic Regression with Python
Linear regression versus logistic regression
Understanding the math behind logistic regression
Implementing logistic regression with Python
Model validation and evaluation
Model validation
Summary
7
Clustering with Python
Clustering with Python
Introduction to clustering – what, why, and how?
Mathematics behind clustering
Implementing clustering using Python
Fine-tuning the clustering
Summary
8
Trees and Random Forests with Python
Trees and Random Forests with Python
Introducing decision trees
Understanding the mathematics behind decision trees
Implementing a decision tree with scikit-learn
Understanding and implementing regression trees
Understanding and implementing random forests
Summary
9
Best Practices for Predictive Modelling
Best Practices for Predictive Modelling
Best practices for coding
Best practices for data handling
Best practices for algorithms
Best practices for statistics
Best practices for business contexts
Summary
A List of Links
A List of Links
Index
Index

IDEs for Python

The IDE or the Integrated Development Environment is a software that provides the source-code editor cum debugger for the purpose of writing code. Using these software, one can write, test, and debug a code snippet before adding the snippet in the production version of the code.

IDLE: IDLE is the default Integrated Development Environment for Python that comes with the default implementation of Python. It comes with the following features:

  • Multi-window text-editor with auto-completion, smart-indent, syntax, and keyword highlighting

  • Python shell with syntax highlighting

IDLE is widely popular as an IDE for beginners; it is simple to use and works well for simple tasks. Some of the issues with IDLE are bad output reporting, absence of line numbering options, and so on. As a result, advanced practitioners move on to better IDEs.

IPython Notebook: IPython Notebook is a powerful computational environment where code, execution, results, and media can co-exist in one single document. There are two components of this computing environment:

  • IPython Notebook: Web applications containing code, executions, plots, and results are stored in different cells; they can be saved and edited as and when required

  • Notebook: It is a plain text document meant to record and distribute the result of a computational analysis

The IPython documents are stored with an extension .ipynb in the directory where it is installed on the computer.

Some of the features of IPython Notebook are as follows:

  • Inline figure rendering of the matplotlib plots that can be saved in multiple formats(JPEG, PNG).

  • Standard Python syntax in the notebook can be saved as a Python script.

  • The notebooks can be saved as HTML files and .ipynb files. These notebooks can be viewed in browsers and this has been developed as a popular tool for illustrated blogging in Python. A notebook in IPython looks as shown in the following screenshot:

    An Ipython Notebook

Spyder: Spyder is a powerful scientific computing and development environment for Python. It has the following features:

  • Advanced editing, auto-completion, debugging, and interactive testing

  • Python kernel and code editor with line numbering in the same screen

  • Preinstalled scientific packages like NumPy, pandas, scikit-learn, matplotlib, and so on.

  • In some ways, Spyder is very similar to RStudio environment where text editing and interactive testing go hand in hand:

    The interface of Spyder IDE

In this book, IPython Notebook and Spyder have been used extensively. IDLE has been used from time to time and some people use other environments, such as Pycharm. Readers of this book are free to use such editors if they are more comfortable with them. However, they should make sure that all the required packages are working fine in those environments.

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