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TensorFlow: Powerful Predictive Analytics with TensorFlow
TensorFlow: Powerful Predictive Analytics with TensorFlow

TensorFlow: Powerful Predictive Analytics with TensorFlow: Predict valuable insights of your data with TensorFlow

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TensorFlow: Powerful Predictive Analytics with TensorFlow

Chapter 2. Putting Data in Place – Supervised Learning for Predictive Analytics

In this lesson, we will discuss supervised learning from the theoretical and practical perspective. In particular, we will revisit the linear regression model for regression analysis discussed in Lesson 1, From Data to Decisions – Getting Started with TensorFlow, using a real dataset. Then we will see how to develop Titanic survival predictive models using Logistic Regression (LR), Random Forests, and Support Vector Machines (SVMs).

In a nutshell, the following topics will be covered in this lesson:

  • Supervised learning for predictive analytics
  • Linear regression for predictive analytics: revisited
  • Logistic regression for predictive analytics
  • Random forests for predictive analytics
  • SVMs for predictive analytics
  • A comparative analysis

Supervised Learning for Predictive Analytics

Depending on the nature of the learning feedback available, the machine learning process is typically classified into three broad categories: supervised learning, unsupervised learning, and reinforcement learning—see figure 1. A predictive model based on supervised learning algorithms can make predictions based on a labelled dataset that map inputs to outputs aligning with the real world.

For example, a dataset for spam filtering usually contains spam messages as well as not-spam messages. Therefore, we could know which messages in the training set are spam and which are ham. Nevertheless, we might have the opportunity to use this information to train our model in order to classify new unseen messages:

Supervised Learning for Predictive Analytics

Figure 1: Machine learning tasks (containing a few algorithms only)

The following figure shows the schematic diagram of supervised learning. After the algorithm has found the required patterns, those patterns can be used to make predictions...

Linear Regression – Revisited

In Lesson 1, From Data to Decisions – Getting Started with TensorFlow we have seen an example of linear regression. We have observed how to work TensorFlow on the randomly generated dataset, that is, fake data. We have seen that the regression is a type of supervised machine learning for predicting the continuous-valued output. However, running a linear regression on fake data is just like buying a new car and never driving it. This awesome machinery begs to manifest itself in the real world!

Fortunately, many datasets are available online to test your new-found knowledge of regression:

Supervised Learning for Predictive Analytics


Depending on the nature of the learning feedback available, the machine learning process is typically classified into three broad categories: supervised learning, unsupervised learning, and reinforcement learning—see figure 1. A predictive model based on supervised learning algorithms can make predictions based on a labelled dataset that map inputs to outputs aligning with the real world.

For example, a dataset for spam filtering usually contains spam messages as well as not-spam messages. Therefore, we could know which messages in the training set are spam and which are ham. Nevertheless, we might have the opportunity to use this information to train our model in order to classify new unseen messages:

Figure 1: Machine learning tasks (containing a few algorithms only)

The following figure shows the schematic diagram of supervised learning. After the algorithm has found the required patterns, those patterns can be used to make predictions for unlabeled...

Linear Regression – Revisited


In Lesson 1, From Data to Decisions – Getting Started with TensorFlow we have seen an example of linear regression. We have observed how to work TensorFlow on the randomly generated dataset, that is, fake data. We have seen that the regression is a type of supervised machine learning for predicting the continuous-valued output. However, running a linear regression on fake data is just like buying a new car and never driving it. This awesome machinery begs to manifest itself in the real world!

Fortunately, many datasets are available online to test your new-found knowledge of regression:

Therefore...

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...

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Key benefits

  • Understand predictive analytics along with its challenges and best practices
  • Embedded with assessments that will help you revise the concepts you have learned in this book

Description

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.

Who is this book for?

This book is aimed at 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.

What you will learn

  • Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration
  • Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics
  • Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics
  • Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations
  • Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets
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Table of Contents

5 Chapters
1. From Data to Decisions – Getting Started with TensorFlow Chevron down icon Chevron up icon
2. Putting Data in Place – Supervised Learning for Predictive Analytics Chevron down icon Chevron up icon
3. Clustering Your Data – Unsupervised Learning for Predictive Analytics Chevron down icon Chevron up icon
4. Using Reinforcement Learning for Predictive Analytics Chevron down icon Chevron up icon
A. Assessment Answers Chevron down icon Chevron up icon
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