How to get started with predictive analytics using Python together with its array of packages for predictive modeling and suites of IDEs to choose from. Ashish Kumar and Joseph Babcock show how analysts can combine Python with sophisticated methods to build scalable analytic applications. The book covers Python libraries such as pandas, scikit-learn, and NumPy, and covers a wide range of algorithms for classification, regression, clustering, as well as techniques such as deep learning.

Authors: Ashish Kumar and Joseph Babcock Date: Dec 2017
Pages: 660
ISBN: 978-1788992367
Print: 1788992369
Kindle: B078NRLXJ2
Audience: Python programmers interested in data analysis
Level: Intermediate
Category: Data Science

• 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
• 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

For recommendations of general books on Python programming see Books for Pythonistas and Python Books For Beginners in our Programmer's Bookshelf section. More books on data analysis are to be found there in  Reading Your Way Into Big Data.

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