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

Linear regression versus logistic regression

One thing to note about the linear regression model is that the output variable is always a continuous variable. In other words, linear regression is a good choice when one needs to predict continuous numbers. However, what if the output variable is a discrete number. What if we want to classify our records in two or more categories? Can we still extend the assumptions of a linear relationship and try to classify the records?

As it happens, there is a separate regression model that takes care of a situation where the output variable is a binary or categorical variable rather than a continuous variable. This model is called logistic regression. In other words, logistic regression is a variation of linear regression where the output variable is a binary or categorical variable. The two regressions are similar in the sense that they both assume a linear relationship between the predictor and output variables. However, as we will see soon, the output...

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