Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications

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
Table of Contents
Getting Started with Predictive Modelling
Data Cleaning
Data Wrangling
Statistical Concepts for Predictive Modelling
Linear Regression with Python
Logistic Regression with Python
Clustering with Python
Trees and Random Forests with Python
Best Practices for Predictive Modelling
A List of Links
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
Bibliography