intermediate Python (knowledge of pandas, NumPy, scikit-learn) • basics of time series methodologies
skills learned
manipulating a dataset with pandas • probabilistic time series analysis with PyMC3 • generating posterior distributions • modeling autoregressive processes
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.
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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 knowledge (pandas, NumPy)
Intermediate scikit-learn
TECHNIQUES
Intermediate probability
Intermediate statistical theory
Intermediate time series methodologies
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