Book Image

Machine Learning Solutions

Book Image

Machine Learning Solutions

Overview of this book

Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.

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Table of Contents (19 chapters)
Machine Learning Solutions
Machine Learning Solutions
Foreword
Foreword
Contributors
Contributors
Preface
Preface
Free Chapter
1
Credit Risk Modeling
Credit Risk Modeling
Introducing the problem statement
Understanding the dataset
Feature engineering for the baseline model
Selecting machine learning algorithms
Training the baseline model
Understanding the testing matrix
Testing the baseline model
Problems with the existing approach
Optimizing the existing approach
Implementing the revised approach
Best approach
Summary
2
Stock Market Price Prediction
Stock Market Price Prediction
Introducing the problem statement
Collecting the dataset
Understanding the dataset
Data preprocessing and data analysis
Feature engineering
Selecting the Machine Learning algorithm
Training the baseline model
Understanding the testing matrix
Testing the baseline model
Exploring problems with the existing approach
Understanding the revised approach
Implementing the revised approach
The best approach
Summary
3
Customer Analytics
Customer Analytics
Introducing customer segmentation
Understanding the datasets
Building the baseline approach
Building the revised approach
The best approach
Customer segmentation for various domains
Summary
4
Recommendation Systems for E-Commerce
Recommendation Systems for E-Commerce
Introducing the problem statement
Understanding the datasets
Building the baseline approach
Building the revised approach
The best approach
Summary
5
Sentiment Analysis
Sentiment Analysis
Introducing problem statements
Understanding the dataset
Building the training and testing datasets for the baseline model
Feature engineering for the baseline model
Selecting the machine learning algorithm
Training the baseline model
Understanding the testing matrix
Testing the baseline model
Problem with the existing approach
How to optimize the existing approach
Implementing the revised approach
The best approach
Summary
6
Job Recommendation Engine
Job Recommendation Engine
Introducing the problem statement
Understanding the datasets
Building the baseline approach
Building the revised approach
The best approach
Summary
7
Text Summarization
Text Summarization
Understanding the basics of summarization
Introducing the problem statement
Understanding datasets
Building the baseline approach
Building the revised approach
The best approach
Summary
8
Developing Chatbots
Developing Chatbots
Introducing the problem statement
Understanding datasets
Building the basic version of a chatbot
Implementing the rule-based chatbot
Testing the rule-based chatbot
Problems with the existing approach
Implementing the revised approach
Testing the revised approach
Problems with the revised approach
The best approach
Discussing the hybrid approach
Summary
9
Building a Real-Time Object Recognition App
Building a Real-Time Object Recognition App
Introducing the problem statement
Understanding the dataset
Transfer Learning
Setting up the coding environment
Features engineering for the baseline model
Selecting the machine learning algorithm
Building the baseline model
Understanding the testing metrics
Testing the baseline model
Problem with existing approach
How to optimize the existing approach
Implementing the revised approach
The best approach
Summary
10
Face Recognition and Face Emotion Recognition
Face Recognition and Face Emotion Recognition
Introducing the problem statement
Setting up the coding environment
Understanding the concepts of face recognition
Approaches for implementing face recognition
Understanding the dataset for face emotion recognition
Understanding the concepts of face emotion recognition
Building the face emotion recognition model
Understanding the testing matrix
Testing the model
Problems with the existing approach
How to optimize the existing approach
The best approach
Summary
11
Building Gaming Bot
Building Gaming Bot
Introducing the problem statement
Setting up the coding environment
Understanding Reinforcement Learning (RL)
Basic Atari gaming bot
Implementing the basic version of the gaming bot
Building the Space Invaders gaming bot
Implementing the Space Invaders gaming bot
Building the Pong gaming bot
Implementing the Pong gaming bot
Just for fun - implementing the Flappy Bird gaming bot
Summary
List of Cheat Sheets
List of Cheat Sheets
Cheat sheets
Summary
Strategy for Wining Hackathons
Strategy for Wining Hackathons
Strategy for winning hackathons
Keeping up to date
Summary
Index
Index

Understanding the testing matrix

In this section, we will understand the testing matrix and visualization approaches to evaluate the performance of the trained ML model. So let's understand both approaches, which are as follows:

  • The default testing matrix

  • The visualization approach

The default testing matrix

We are using the default score API of scikit-learn to check how well the ML is performing. In this application, the score function is the coefficient of the sum of the squared error. It is also called the coefficient of R2, which is defined by the following equation:

Here, u indicates the residual sum of squares. The equation for u is as follows:

The variable v indicates the total sum of squares. The equation for v is as follows:

The best possible score is 1.0, and it can be a negative score as well. A negative score indicates that the trained model can be arbitrarily worse. A constant model that always predicts the expected value for label y, disregarding the input features, will produce an...

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