In this liveProject, you’ll use PyMC3 to generate a posterior distribution of hotel cancellations. This will allow you to build a predictive model that can update its predicted probabilities by incorporating new information. You’ll master methods for modelling mean and standard deviations based on the selected prior values, generating distributions to determine if data follows an AR(1) process, modeling of stochastic volatility and generalized linear modelling with PyMC3.
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 knowledge (pandas, NumPy)
- Intermediate scikit-learn
- Intermediate probability
- Intermediate statistical theory
- Intermediate time series methodologies
you will learn
In this liveProject, you’ll learn to generate Bayesian models based on numerous components of a dataset.
- Generating posterior distributions
- Modeling autoregressive processes
- Volatility modeling
- Bayesian generalized linear modeling