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
Credits
Preface

From Disaster to Decision – Titanic Example Revisited


In Lesson 1, From Data to Decisions – Getting Started with TensorFlow, we have seen a minimal data analysis of the Titanic dataset. Now it's our turn to do some analytics on top of the data. Let's look at what kinds of people survived the disaster.

Since we have enough data, but how could we do the predictive modeling so that we can draw some fairly straightforward conclusions from this data? For example, being a woman, being in first class, and being a child were all factors that could boost a passengers chances of survival during this disaster.

Using the brute-force approach such as if-else statements with some sort of weighted scoring system, you could write a program to predict whether a given passenger would survive the disaster. However, writing such a program in Python does not make much sense. Naturally, it would be very tedious to write, difficult to generalize, and would require extensive fine-tuning for each variable and samples...