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

by Alvaro Fuentes

Overview

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.

Product Details

ISBN-13:	9781789134544
Publisher:	Packt Publishing
Publication date:	12/28/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	330
File size:	9 MB

About the Author

Alvaro Fuentes is a data scientist with more than 12 years of experience in analytical roles. He holds an M.S. in applied mathematics and an 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, medicine, and mass media, among others. He is a big Python fan and has been using it routinely for five years to analyze data, build models, produce reports, make predictions, and build interactive applications that transform data into intelligence.

Table of Contents

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