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

A step-by-step guide to building high performing predictive applications

Key Features

• Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects
• Get up to speed with advanced predictive modeling algorithms with the help of detailed explanations
• Learn to present a predictive model's results as an interactive application

Book Description

Predictive analytics is a field of applied analytics that employs a variety of quantitative methods to analyze your data and make predictions. This book guides you through the most important concepts related to predictive analytics.

With the help of practical, step-by-step examples, you'll be able to build predictive analytics solutions while using cutting-edge Python tools and packages. You'll learn effectively by defining the problem and then moving on to identifying relevant data. As you advance, you'll get to grips with tasks such as data preparation, exploring and visualizing relationships, building models, and more. You will also work with models such as K-Nearest Neighbors (KNN), random forests, and neural networks using key libraries in Python's data science stack including NumPy, pandas, Matplotlib, and Seaborn. All along, you'll explore useful examples and Python code that will help you grasp the concepts and techniques effectively. In addition to this, you'll gain detailed insights into the core techniques and algorithms used in predictive analytics.

By the end of this book, you will be equipped with the skills you need to build high-performance predictive analytics solutions using Python programming.

What you will learn

• Get to grips with the core concepts and principles of predictive analytics
• Explore 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 a dataset
• Use Keras to build powerful neural network models that produce accurate predictions
• Build regression and classification models using scikit-learn

Who this book is for

This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and are interested in implementing predictive analytics solutions using Python's data stack. Anyone looking to get started in this exciting field will also find this book useful. Proficiency in Python programming and a basic understanding of statistics and college-level algebra are required.

Table of Contents

1. The Predictive Analytics Process
2. Problem Understanding and Data Preparation
3. Dataset Understanding - Exploratory Data Analysis
4. Predicting Numerical Values with Machine Learning
5. Predicting Categories with Machine Learning
6. Introducing Neural Nets for Predictive Analytics
7. Model Evaluation
8. Model Tuning and Improving Performance
9. Implementing a Model with Dash

   About the Author

   Alvaro Fuentes is a Data Scientist with more than 12 years of experience in analytical roles, he holds a M.S. in Applied Mathematics and a M.S. in Quantitative Economics. He worked for many years in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as: Business, Education, Psychology, Mass Media, among others. For the past 3 years he also has taught many courses (online in different platforms and in-site) to hundreds of students from around the world in topics like Data Science, Mathematics, Statistics, Machine Learning, R and Python programming.He is a big Python fan and has been using it routinely for five years for analyzing data, building models, producing reports, making predictions and build interactive applications that transform data into intelligence. Alvaro s technical skills include Python scientific computing stack, R programming, Spark, PostgreSQL, MS Excel, machine learning, artificial intelligence applications, statistical analysis, econometrics and mathematical modeling.Bayesian Statistics is a topic in which he has both professional and teaching experience. Having solved practical problems in his consulting practice applying Bayesian methods to solve problems in both academia and industry.