Book Image

Hands-On Predictive Analytics with Python

By : Alvaro Fuentes
Book Image

Hands-On Predictive Analytics with Python

By: Alvaro Fuentes

Overview of this book

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.
Table of Contents (11 chapters)

Summary

In this chapter, we introduced the most fundamental type of deep learning model—the MLP. We covered a lot of new concepts related to this power class of models such as deep learning, neural network models, and the activation functions of neurons. We also learned about TensorFlow, which is a framework to train deep learning models; we used it as a backend for running the calculations necessary to train our models. We covered Keras, where we first build a network, and then we compile it (indicating the loss and optimizer), and finally, we train the model. Lastly, we covered dropout, which is a regularization technique that is often used with neural networks, although it works best with very large networks. To conclude, neural networks are hard to train because they involve making many decisions; a lot of practice and knowledge is needed to be able to use these models...