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
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 would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.
Table of Contents
- The Predictive Analytics Process
- Problem Understanding and Data Preparation
- Dataset Understanding - Exploratory Data Analysis
- Predicting Numerical Values with Machine Learning
- Predicting Categories with Machine Learning
- Introducing Neural Nets for Predictive Analytics
- Model Evaluation
- Model Tuning and Improving Performance
- Implementing a Model with Dash
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.
