Book Image

TensorFlow: Powerful Predictive Analytics with TensorFlow

By : Md. Rezaul Karim
Book Image

TensorFlow: Powerful Predictive Analytics with TensorFlow

By: Md. Rezaul Karim

Overview of this book

Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google’s brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. This book is repurposed for this specific learning experience from material from Packt's Predictive Analytics with TensorFlow by Md. Rezaul Karim.
Table of Contents (8 chapters)
TensorFlow: Powerful Predictive Analytics with TensorFlow

Using kNN for Predictive Analytics

kNN is non-parametric and instance-based and is used in supervised learning. It is a robust and versatile classifier, frequently used as a benchmark for complex classifiers such as Neural Networks (NNs) and Support Vector Machines (SVMs). kNN is commonly used in economic forecasting, data compression, and genetics based on their expression profiling.

Working Principles of kNN

The idea of kNN is that from a set of features x we try to predict the labels y. Thus, kNN falls in a supervised learning family of algorithms. Informally, this means that we are given a labeled dataset consisting of training observations (x, y). Now, the task is to model the relationship between x and y so that the function f: X→Y learns from the unseen observation x. The function f(x) can confidently predict the corresponding label y prediction on a point z by looking at a set of nearest neighbors.

However, the actual method of prediction depends on whether or not we are doing regression...