Predictive Analytics for the Modern Enterprise

Name: Predictive Analytics for the Modern Enterprise
Brand: O'Reilly
Price: 50.49 EUR
Availability: OnlineOnly

Nooruddin Abbas Ali(Autor*in)

O'Reilly (Verlag)

Erschienen am 20. Mai 2024

360 Seiten

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978-1-0981-3683-3 (ISBN)

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Inhalt

Intro
Copyright
Table of Contents
Preface
Who Is This Book For?
How This Book Is Organized
Conventions Used in This Book
Using Code Examples
O'Reilly Online Learning
How to Contact Us
Acknowledgments
Chapter 1. Data Analytics in the Modern Enterprise
The Evolution of Data Analytics
Different Types of Data Analytics
Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics
Knowledge Acquisition, Machine Learning, and the Role of Predictive Analytics
Tools, Frameworks, and Platforms in the Predictive Analytics World
Languages and Libraries
Services
Conclusion
Chapter 2. Predictive Analytics: An Operational Necessity
The Move from "Data Producing" to "Data Driven"
Challenges to Using Predictive Analytics
People
Data
Technology
Vertical Industry Use Cases for Predictive Analytics
Finance
Healthcare
Automotive
Entertainment
Conclusion
Chapter 3. The Mathematics and Algorithms Behind Predictive Analytics
Statistics and Linear Algebra
Regression
What Is Regression Analysis?
Regression Techniques
R-squared and P-value
Selecting a Regression Model
Decision Trees
Training Decision Trees
Using Decision Trees to Solve Regression Problems: Regression Trees
Tuning Decision Trees
Other Algorithms
Random Forests
Neural Networks
Support Vector Machines
Naive Bayes Classifier
Other Learning Patterns in Machine Learning
Conclusion
Chapter 4. Working with Data
Understanding Data
Data Preprocessing and Feature Engineering
Handling Missing Data
Categorical Data Encoding
Data Transformation
Outlier Management
Handling Imbalanced Data
Combining Data
Feature Selection
Splitting Preprocessed Data
Understanding Bias
The Predictive Analytics Pipeline
The Data Stage
The Model Stage
The Serving Stage
Other Components
Selecting the Right Model
Conclusion
Chapter 5. Python and scikit-learn for Predictive Analytics
Anaconda and Jupyter Notebooks
NumPy in Python
Introduction to NumPy
Generating Arrays
Array Slicing
Array Transformation
Other Array Operations
Exploring a Business Example Using Pandas
Pandas in Python
Import and View Data
Visualize the Data
Data Cleaning and Modification
Reading from Different Data Sources
Data Filtering and Grouping
Scikit-learn
Training and Predicting with a Linear Regression Model
Using a Random Forest Classifier
Training a Decision Tree
A Clustering Example (Unsupervised Learning)
Conclusion
Chapter 6. TensorFlow and Keras for Predictive Analytics
TensorFlow Fundamentals
Linear Regression Using TensorFlow
Data Preparation
Model Creation and Training
Predictions and Model Evaluation
Deep Neural Networks in TensorFlow
Conclusion
Chapter 7. Predictive Analytics for Business Problem-Solving
Prediction-Based Optimal Retail Price Recommendations
Using a Simple Linear Regression Model
Using a Polynomial Regression Model
Using Multivariate Regression
An Introduction to Recommender Systems
Building Recommender Systems Using surprise scikit in Python
Credit Card Fraud Classification
Credit Card Fraud Baseline Analysis Using Artificial Neural Networks
Credit Card Fraud Weighted Analysis Using Artificial Neural Networks
Credit Card Analysis with Multiple Hidden Layers in the Artificial Neural Network
Conclusion
Chapter 8. Exploring AWS Cloud Provider Services for AI/ML
To Cloud or Not to Cloud
Exploring AWS SageMaker
Prerequisites
Data Ingest and Exploration
Data Transformation
Model Training and Prediction
Cleanup
Exploring Amazon Forecast
Import Data
Train the Predictor
Create a Forecast
What-If Analysis
Cleanup
Conclusion
Chapter 9. Food for Thought
A Few More Use Cases
Navigation and Traffic Management
Credit Scoring
The Social Impact of Predictions
Conclusion
Index
About the Author
Colophon

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