Predictive analytics for the modern enterprise a practiioner's guide to designing and implementing solutions
The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in t...
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| Format: | eBook |
| Language: | English |
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Sebastopol, CA
O'Reilly Media, Inc.
2024
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| Edition: | First edition |
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- 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
- Includes bibliographical references and index
- 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
- 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
- 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?
- 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