Data Science Notebooks
Explore our Jupyter Notebooks to making getting started with PredictHQ data easier.
Demand intelligence is a new and evolving field. We’ve created a number of Data Science Jupyter Notebooks to help you get started with PredictHQ’s intelligent event data. You’ll find guides to using our API with common Data Science tools, libraries in Python, and code samples.
Forecasts API Notebook
Use our new Forecasts API to train a model in a few simple steps and focus on making use of the forecast results. Our API uses the same underlying XGBoost-based algorithm as the following notebook.
PredictHQ Forecast Models Notebook
Train an XGBoost model incorporating PredictHQ event features to quickly validate the impact of event-driven forecasting and best practice. An easier approach is to use our new Forecasts API which will get you the same result - and more - in a fraction of the time.
Beam Notebooks
Designed to provide you with the context you need to get started with the Beam API to find relevant events and use it effectively.
Features API Notebook
A how-to guide with details on how to retrieve machine learning features using Features API.
Demand Forecasting Notebook
Step-by-step guide on how to use PredictHQ event features in your existing demand forecasting models.
Attendance-Based Events Notebooks
Attended Events are scheduled to occur at a specific location and usually depend on attendance, such as conferences, expos, concerts, festivals, performing-arts, sports and community.
Non-Attendance-Based Events Notebooks
Non-Attendance-Based Events are events with a start and end date, but are more fluid and distributed in impact, such as observances or school holidays.
Severe Weather Event Notebooks
Severe weather is any dangerous meteorological phenomenon with the potential to cause damage, serious social disruption, or loss of human life.
Academic Events Notebooks
Academic Events are captured from an individual higher education institute’s academic calendar. They outline the general undergraduate activities, for example instruction period, break, exams, graduation, social, etc.
All our Data Science Notebooks can be found in our GitHub repo.
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