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 a part of the series Time Series Forecasting with Bayesian Modeling.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

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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