Prophet Model Incorporated with Bayesian Analysis

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prerequisites
intermediate Python knowledge (pandas, NumPy) • intermediate scikit-learn
skills learned
implementing prophet models • configuring changepoints • analyzing time series data with uncertainty interval configuration
Michael Grogan
1 week · 4-6 hours per week · ADVANCED
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In this liveProject, you’ll build a Prophet model that can forecast airline passenger numbers using data from the DataSF portal. The hotel you work for believes that analyzing the travel trends of US customers will help them forecast potential travel to Europe, and bookings in the hotel. You’ll enhance your model with "changepoints" that mark a significant change in trends, and make adjustments so your model can account for uncertainty in the trend and seasonality components.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Michael Grogan
Michael Grogan is a data scientist with expertise in TensorFlow and time series analysis. His educational background is a Master's degree in Economics from University College Cork, Ireland. As such, much of his work has been in the domain of business intelligence, i.e. using machine learning technologies to develop solutions to a wide range of business problems. He has implemented time series solutions for organizations across a range of industries through the implementation of statistical analysis as well as more advanced machine learning methodologies. In addition, he has delivered numerous seminars and training courses in the areas of data science and machine learning, including for Manning and O'Reilly Media.

prerequisites

This liveProject is for data analysts with an intermediate understanding of time series analysis. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Intermediate pandas and NumPy
  • Basics of scikit-learn
TECHNIQUES
  • Basics of time series methodologies

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