Machine Learning Solutions

Jalaj Thanaki

• Editorial: Packt Publishing
• Año: 2018
• ISBN:
  9781788398893
• Edición: 1
• Categoría: Informatik
• Categoría: Künstliche Intelligenz (KI) & Semantik

Practical, hands-on solutions in Python to overcome any problem in Machine LearningAbout This Book• Master the advanced concepts, methodologies, and use cases of machine learning• Build ML applications for analytics, NLP and computer vision domains• Solve the most common problems in building machine learning modelsWho This Book Is ForThis book is for the intermediate users such as machine learning engineers, data engineers, data scientists, and more, who want to solve simple to complex machine learning problems in their day-to-day work and build powerful and efficient machine learning models. A basic understanding of the machine learning concepts and some experience with Python programming is all you need to get started with this book.What You Will Learn• Select the right algorithm to derive the best solution in ML domains• Perform predictive analysis effciently using ML algorithms• Predict stock prices using the stock index value• Perform customer analytics for an e-commerce platform• Build recommendation engines for various domains• Build NLP applications for the health domain• Build language generation applications using different NLP techniques• Build computer vision applications such as facial emotion recognitionIn DetailMachine 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.Style and approachThis book is a step-by-step guide on how to develop machine learning applications for various domains. Each chapter of this book contains the practical guide on how to build specific machine learning applications from its base-line approach to the best possible approach. Basic necessary concepts, conman mistakes for every approach and optimization techniques are discussed for each application.

Machine Learning Solutions

---

Table of Contents

Machine Learning Solutions

Why subscribe?

PacktPub.com

Foreword

Contributors

About the author

About the reviewer

Packt is Searching for Authors Like You

Preface

Who this book is for

What this book covers

To get the most out of this book

Download the example code files

Conventions used

Get in touch

Reviews

1. Credit Risk Modeling

Introducing the problem statement

Understanding the dataset

Understanding attributes of the dataset

Data analysis

Data preprocessing

First change

Second change

Implementing the changes

Basic data analysis followed by data preprocessing

Listing statistical properties

Finding missing values

Replacing missing values

Correlation

Detecting outliers

Outliers detection techniques

Percentile-based outlier detection

Median Absolute Deviation (MAD)-based outlier detection

Standard Deviation (STD)-based outlier detection

Majority-vote-based outlier detection:

Visualization of outliers

Handling outliers

Revolving utilization of unsecured lines

Age

Number of time 30-59 days past due not worse

Debt ratio

Monthly income

Number of open credit lines and loans

Number of times 90 days late

Number of real estate loans or lines

Number of times 60-89 days past due not worse

Number of dependents

Feature engineering for the baseline model

Finding out Feature importance

Selecting machine learning algorithms

K-Nearest Neighbor (KNN)

Logistic regression

AdaBoost

GradientBoosting

RandomForest

Training the baseline model

Understanding the testing matrix

The Mean accuracy of the trained models

The ROC-AUC score

ROC

AUC

Testing the baseline model

Problems with the existing approach

Optimizing the existing approach

Understanding key concepts to optimize the approach

Cross-validation

The approach of using CV

Hyperparameter tuning

Grid search parameter tuning

Random search parameter tuning

Implementing the revised approach

Implementing a cross-validation based approach

Implementing hyperparameter tuning

Implementing and testing the revised approach

Understanding problems with the revised approach

Best approach

Implementing the best approach

Log transformation of features

Voting-based ensemble ML model

Running ML models on real test data

Summary

2. Stock Market Price Prediction

Introducing the problem statement

Collecting the dataset

Collecting DJIA index prices

Collecting news articles

Understanding the dataset

Understanding the DJIA dataset

Understanding the NYTimes news article dataset

Data preprocessing and data analysis

Preparing the DJIA training dataset

Basic data analysis for a DJIA dataset

Preparing the NYTimes news dataset

Converting publication date into the YYYY-MM-DD format

Filtering news articles by category

Implementing the filter functionality and merging the dataset

Saving the merged dataset in the pickle file format

Feature engineering

Loading the dataset

Minor preprocessing

Converting adj close price into the integer format

Removing the leftmost dot from news headlines

Feature engineering

Sentiment analysis of NYTimes news articles

Selecting the Machine Learning algorithm

Training the baseline model

Splitting the training and testing dataset

Splitting prediction labels for the training and testing datasets

Converting sentiment scores into the numpy array

Training of the ML model

Understanding the testing matrix

The default testing matrix

The visualization approach

Testing the baseline model

Generating and interpreting the output

Generating the accuracy score

Visualizing the output

Exploring problems with the existing approach

Alignment

Smoothing

Trying a different ML algorithm

Understanding the revised approach

Understanding concepts and approaches

Alignment-based approach

Smoothing-based approach

Logistic Regression-based approach

Implementing the revised approach

Implementation

Implementing alignment

Implementing smoothing

Implementing logistic regression

Testing the revised approach

Understanding the problem with the revised approach

The best approach

Summary

3. Customer Analytics

Introducing customer segmentation

Introducing the problem statement

Understanding the datasets

Description of the dataset

Downloading the dataset

Attributes of the dataset

Building the baseline approach

Implementing the baseline approach

Data preparation

Loading the dataset

Exploratory data analysis (EDA)

Removing null data entries

Removing duplicate data entries

EDA for various data attributes

Country

Customer and products

Product categories

Analyzing the product description

Defining product categories

Characterizing the content of clusters

Silhouette intra-cluster score analysis

Analysis using a word cloud

Principal component analysis (PCA)

Generating customer categories

Formatting data

Grouping products

Splitting the dataset

Grouping orders

Creating customer categories

Data encoding

Generating customer categories

PCA analysis

Analyzing the cluster using silhouette scores

Classifying customers

Defining helper functions

Splitting the data into training and testing

Implementing the Machine Learning (ML) algorithm

Understanding the testing matrix

Confusion matrix

Learning curve

Testing the result of the baseline approach

Generating the accuracy score for classifier

Generating the confusion matrix for the classifier

Generating the learning curve for the classifier

Problems with the baseline approach

Optimizing the baseline approach

Building the revised approach

Implementing the revised approa...

Índice

• Machine Learning Solutions