In many datasets, the relationship between our inputs and output may not be a straight line. For example, consider the relationship between hour of the day and probability of posting on social media. If you were to draw a plot of this probability, it would likely increase during the evening and lunch break, and decline during the night, morning and workday, forming a sinusoidal wave pattern. A linear model cannot represent this kind of relationship, as the value of the response does not strictly increase or decrease with the hour of the day. What models, then, could we use to capture this relationship? In the specific case of time series models we could use approaches such as the Kalman filter described above, using the components of the structural time series equation to represent the cyclical 24-hour pattern of social media activity. In the following section we examine more general approaches that will apply both to time series data and to more general non-linear relationships...
Mastering Predictive Analytics with Python
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Mastering Predictive Analytics with Python
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Overview of this book
The volume, diversity, and speed of data available has never been greater. Powerful machine learning methods can unlock the value in this information by finding complex relationships and unanticipated trends. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications to deliver insights that are of tremendous value to their organizations.
In Mastering Predictive Analytics with Python, you will learn the process of turning raw data into powerful insights. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications and how to quickly apply these methods to your own data to create robust and scalable prediction services.
Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates not only how these methods work, but how to implement them in practice. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring the insights of predictive modeling to life
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Table of Contents (16 chapters)
Mastering Predictive Analytics with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
From Data to Decisions – Getting Started with Analytic Applications
Exploratory Data Analysis and Visualization in Python
Finding Patterns in the Noise – Clustering and Unsupervised Learning
Connecting the Dots with Models – Regression Methods
Putting Data in its Place – Classification Methods and Analysis
Words and Pixels – Working with Unstructured Data
Learning from the Bottom Up – Deep Networks and Unsupervised Features
Sharing Models with Prediction Services
Reporting and Testing – Iterating on Analytic Systems
Index
Customer Reviews