9. Predictive Analytics

Table of Contents

• 9.1. Introduction
  • 9.1.1. Learning Goals
  • 9.1.2. Learning Objectives
• 9.2. Predicting Bike Rentals
• 9.3. Exploring Bike Rental Data with SQL
  • 9.3.1. Query Language
• 9.4. Getting Started with the Bike Data
  • 9.4.1. Verify access to the dataset
• 9.5. Filtering
  • 9.5.1. Practice Exercises
• 9.6. Sorting
  • 9.6.1. Practice Exercises
• 9.7. Aggregation or Group By
  • 9.7.1. Practice Exercises
• 9.8. Joining
  • 9.8.1. Practice Exercises
• 9.9. Getting SQL Data into a DataFrame
  • 9.9.1. Mapping Bike Stations Using Anaconda
  • 9.9.2. Practice and Further Exploration
• 9.10. Mapping Bike Stations Using Colab
  • 9.10.1. Practice and Further Exploration
• 9.11. Visualizing Bike Share Data as a Time Series
  • 9.11.1. Indexing with a DatetimeIndex
  • 9.11.2. Next Step: Predicting Rentals
  • 9.11.3. Working with ZIP Files (Optional)
• 9.12. Predicting Pizza Prices - Linear Regression
  • 9.12.1. Closed-Form Solution
  • 9.12.2. The Payoff - Supervised Learning
  • 9.12.3. A Simple Machine Learning Approach (Optional)
• 9.13. Improving our Pizza Price Predictions
  • 9.13.1. Introducing Scikit-Learn
  • 9.13.2. Practice
• 9.14. Predicting Daily Bike Rentals
  • 9.14.1. The Train-Test Split
  • 9.14.2. Evaluating the Model
  • 9.14.3. Feature Engineering
  • 9.14.4. Version 1.0 Task List
  • 9.14.5. Refining the Model
  • 9.14.6. Version 2.0 Task List
  • 9.14.7. Version 2.5 Task List
  • 9.14.8. Version 3.0
  • 9.14.9. Feature Engineering - Re-Scaling
  • 9.14.10. Where to go from here?
• 9.15. Glossary
  • 9.15.1. Definitions
  • 9.15.2. Keywords

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