Book description
Gain practical insights by exploiting data in your business to build advanced predictive modeling applications
About This Book
 A stepbystep 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
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.
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, subsetting, 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
In Detail
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, scikitlearn, and NumPy. Through case studies and code examples using popular opensource 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 cuttingedge 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
Style and approach
This course aims to create a smooth learning path that will teach you how to effectively perform predictive analytics using Python. Through this comprehensive course, you'll learn the basics of predictive analytics and progress to predictive modeling in the modern world.
Publisher resources
Table of contents

Python: Advanced Predictive Analytics
 Table of Contents
 Python: Advanced Predictive Analytics
 Credits
 Preface

1. Module 1
 1. Getting Started with Predictive Modelling

2. Data Cleaning
 Reading the data – variations and examples
 Various methods of importing data in Python
 Basics – summary, dimensions, and structure
 Handling missing values
 Creating dummy variables
 Visualizing a dataset by basic plotting
 Summary

3. 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
 5. Linear Regression with Python

6. 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
 8. Trees and Random Forests with Python
 9. Best Practices for Predictive Modelling
 A. A List of Links

2. Module 2
 1. From Data to Decisions – Getting Started with Analytic Applications
 2. Exploratory Data Analysis and Visualization in Python
 3. Finding Patterns in the Noise – Clustering and Unsupervised Learning
 4. Connecting the Dots with Models – Regression Methods
 5. Putting Data in its Place – Classification Methods and Analysis
 6. Words and Pixels – Working with Unstructured Data

7. Learning from the Bottom Up – Deep Networks and Unsupervised Features

Learning patterns with neural networks
 A network of one – the perceptron
 Combining perceptrons – a singlelayer neural network
 Parameter fitting with backpropagation
 Discriminative versus generative models
 Vanishing gradients and explaining away
 Pretraining belief networks
 Using dropout to regularize networks
 Convolutional networks and rectified units
 Compressing Data with autoencoder networks
 Optimizing the learning rate
 The TensorFlow library and digit recognition
 Summary

Learning patterns with neural networks
 8. Sharing Models with Prediction Services
 9. Reporting and Testing – Iterating on Analytic Systems
 Bibliography
 Index
Product information
 Title: Python: Advanced Predictive Analytics
 Author(s):
 Release date: December 2017
 Publisher(s): Packt Publishing
 ISBN: 9781788992367
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