Gain practical insights by exploiting data in your business to build advanced predictive modeling applications
Key Features
A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices
Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering
Master open source Python tools to build sophisticated predictive models
Book Description 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. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python.
You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling.
Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books:
1. Learning Predictive Analytics with Python
2. Mastering Predictive Analytics with Python
What you will learn
Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries
Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy
Master the use of Python notebooks for exploratory data analysis and rapid prototyping
Get to grips with applying regression, classification, clustering, and deep learning algorithms
Discover advanced methods to analyze structured and unstructured data
Visualize the performance of models and the insights they produce
Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis
Who this book is for This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move on from a conceptual understanding of advanced analytics and become an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you.
Author Biography:
Ashish Kumar has a B. Tech from IIT Madras and is a Young India Fellow from the batch of 2012-13. He is a data science enthusiast with extensive work experience in the field. As a part of his work experience, he has worked with tools such as Python, R, and SAS. He has also implemented predictive algorithms to glean actionable insights for clients from the transport and logistics, online payment, and healthcare industries. Apart from the data sciences, he is enthused by and adept at financial modeling and operational research. He is a prolific writer and has authored several online articles and short stories apart from running his own analytics blog. He also works pro-bono for a couple of social enterprises and freelances his data science skills. His twitter handle is @asis64 Joseph Babcock has spent almost a decade exploring complex datasets and combining predictive modeling with visualization to understand correlations and forecast anticipated outcomes. He received a PhD from the Solomon H. Snyder Department of Neuroscience at The Johns Hopkins University School of Medicine, where he used machine learning to predict adverse side-effects of cardiac drugs. Outside the academy, he has tackled big data challenges in the healthcare and entertainment industries.
