Predictive Analytics for the Modern Enterprise

Spis treści ebooka

• Preface
  • Who Is This Book For?
  • How This Book Is Organized
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 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
      • AWS services
      • GCP services
  • Conclusion
• 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
• 3. The Mathematics and Algorithms Behind Predictive Analytics
  • Statistics and Linear Algebra
  • Regression
    • What Is Regression Analysis?
    • Regression Techniques
      • Linear regression
      • Polynomial regression
      • Logistic regression
    • 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
• 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
      • Feature store
      • Model registry
  • Selecting the Right Model
  • Conclusion
• 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
• 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
• 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
      • K-nearest neighbor algorithm
        • Euclidean distance
        • Manhattan distance
        • Hamming distance
        • Cosine similarity
      • kNN-based recommender systems
      • Two-stage recommender system
        • Retriever stage
        • Ranker stage
  • 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
• 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
• 9. Food for Thought
  • A Few More Use Cases
    • Navigation and Traffic Management
    • Credit Scoring
  • The Social Impact of Predictions
  • Conclusion
• Index

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