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.
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:
- Intermediate Python
- Intermediate pandas and NumPy
- Basics of scikit-learn
- Basics of time series methodologies
you will learn
In this liveProject, you’ll develop Prophet models to make predictions across seasonal data, and enhance the generated forecasts with the use of Bayesian techniques.
- Prophet model implementation
- Manual changepoint configuration
- Analysis of uncertainty intervals