Learning Predictive Analytics With Python: Gain Practical Insights into Predictive Modelling by Implementing Predictive Analytics Algorithms on Public Datasets With Python

This is a good book. I do not understand why there are bad reviews for it. I would like to thank the author for the good job! Well done! Unfortunately, the author deleted the datasets the book uses from the Google drive.

The author is writing about using Python, but even the simplest of Python examples are incorrect.For example, the author fails to explain how Python stores and references elements of arrays -- they are zero-based -- then writes examples assuming storage is one-based that result in incorrect results. This is not a single typo. The error is repeated throughout the book. For example, from page 57:"If one wants to select the first 50 rows of the data frame, one can just write: data[1:50]"Experienced Python programmers will know the result will be 49 values, from array locations 1 through 49, but not including the first row, which is row 0.Throughout the book, code is written to be examples, but the result of executing the code is seldom shown. Indeed, if the code had been run and the result shown, the result would immediately illustrate that the code was incorrect.Throughout the book, the grammar is awkward, punctuation confusing, charts inaccurate, programming non-standard, text does not match illustration, context is changed without warning, etc.The sections on predictive analytics and interpretation of results include discussions that are simplistic and discussions that are overly complex. There is very little that will help readers new to the field.I wanted to like this book. I teach machine learning and I was hoping this would be a book I could recommend to my students. Three previously posted reviews give the book five stars. After my experience, I doubt that any of the three read the book. Certainly none tried to run any of the code. One reviewer gave the book one star, but based that score on the author's choice of Python 2 rather than Python 3. While Python 3 is more recent, not all of the support libraries have been converted from Python 2 to Python 3, and many modelers continue to use Python 2.I dislike posting bad reviews. But this book is at very best a rough draft. The author, the Packt editors, the writer of the foreword, and the reviewer of the text, all know this book is not ready for publication and should have sent it back for revision.

Good Content. Need immediate check for typos (in code) and revisions for latest py versions.

Poor English and lots of typos.

If you are familiar with Packt (the publisher), you will know that they tend to carpet bomb particular areas, with multiple overlapping titles. This makes it difficult to recommend just one title if anyone asks you, since different books have different strengths.The strength of this book is that the author really does explain how to use PANDAS (python data analysis library) and statistical analysis from the ground up. Most pandas users will be familiar with pd.read_csv, but he covered a lot of options that I had never really understood properly, because I chiefly learnt from examples that don't really give you the 'why' of things.You might say, why not read the original book by Wes McKinney? I would have to say that this is a much more interesting read and has better flow. The Wes McKinney book sometimes reads like documentation and you are not sure what to really focus on.The coverage of statistical learning is also good, for instance he does a nice explanation of logistic regression and the underlying methodology with just enough math to properly explain the distinction between linear regression and logistic regression.I think the book is thorough enough that you could actually use it as a coursebook for statistical learning w/python, which a high praise for a book with a fairly generic title.

You don't have to be married to a physicist to appreciate the role of the team at CERN that confirmed the existence of the Higgs Boson. Who better to be a reviewer of this book than a member of that team? That fact itself should inspire confidence in the utility of this book. The author uses interesting analogies to explain the different aspects of predictive analytics and even goes so far as to present comparison tables, serving to drive home his points. The ease and power of the Python programming language is put to good use in explaining the process of data cleaning and wrangling. The better part of the first half of the book is dedicated to exploring the various aspects of these two critical processes with easy to follow examples and code. A whole chapter is devoted to laying out the statistical concepts that are integral to getting the most out of the remainder of the book. The latter part of the book details supervised and unsupervised predictive modelling algorithms, shows how to implement them in Python and furthermore, delves deep into the mathematics of these widely used algorithms so that readers become well equipped to tackle real world challenges of predictive analytics in ANY programming language of their choice. In my opinion, the author really succeeded in making the serious subject matter of this book sound cool and exciting.