Hands-On Predictive Analytics with Python: Master the complete predictive analytics process, from problem definition to model deployment

Step-by-step guide to build high performing predictive applications

Key Features

• Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
• Explore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanations
• Learn to deploy a predictive model's results as an interactive application

Book Description

Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages.

The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model.

Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics.

By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming.

What you will learn

• Get to grips with the main concepts and principles of predictive analytics
• Learn about the stages involved in producing complete predictive analytics solutions
• Understand how to define a problem, propose a solution, and prepare a dataset
• Use visualizations to explore relationships and gain insights into the dataset
• Learn to build regression and classification models using scikit-learn
• Use Keras to build powerful neural network models that produce accurate predictions
• Learn to serve a model's predictions as a web application