What this book covers

Chapter 1, The Predictive Analytics Process, presents the foundational concepts of the field, explains at a high level the different stages in the predictive analytics process, and gives an overview of the libraries we will use in the book.

Chapter 2, Problem Understanding and Data Preparation, introduces the problems and datasets we will be using throughout the book and shows the basics of how to collect and prepare a dataset for modeling.

Chapter 3, Dataset Understanding – Exploratory Data Analysis, shows how to get important information from a dataset using visualizations and other numerical techniques.

Chapter 4, Predicting Numerical Values with Machine Learning, introduces the main ideas and concepts of machine learning and some of the most popular regression models.

Chapter 5, Predicting Categories with Machine Learning, introduces some of the most important classification machine learning models.

Chapter 6, Introducing Neural Nets for Predictive Analytics, shows how to build neural network models. These have become very popular because they are very powerful and are capable of producing highly accurate models. 

Chapter 7, Model Evaluation, shows the main metrics and approaches you need to evaluate how good the predictions produced by a predictive model are.

Chapter 8, Model Tuning and Improving Performance, presents important techniques such as K-fold cross-validation that will improve the performance of our predictive model.

Chapter 9, Implementing a Model with Dash, shows how to build an interactive web application that will take input from the user and will use a trained predictive model to provide predictions.