Look inside
In this liveProject, you’ll investigate seasonality in hotel cancellations by building an ARIMA model that can predict cancellations on a weekly basis. You’ll learn how to manipulate a dataset with pandas in order to form a weekly time series, before going on to make your first predictions.
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 a basic understanding of time series methods and data manipulation tools in Python including pandas. To begin this liveProject, you will need to be familiar with:
TOOLS
- Intermediate knowledge of Python, particularly the pandas, NumPy, and sklearn libraries
TECHNIQUES
- Intermediate time series methodologies
you will learn
In this liveProject, you’ll learn how to implement a time series forecast through analysis of seasonality and trend patterns, along with proper configuration of ARIMA model parameters.
- Manipulate a dataset with pandas
- Implement time series forecasts with pmdarima
features
- Self-paced
- You choose the schedule and decide how much time to invest as you build your project.
- Project roadmap
- Each project is divided into several achievable steps.
- Get Help
- While within the liveProject platform, get help from other participants.
- Compare with others
- For each step, compare your deliverable to the solutions by the author and other participants.