lørdag 4. juni 2016

Using a predictive analytics model to foresee flight delays

Imagine an app that can predict flight delays. The ebook, Using a Predictive Analytics Model to Foresee Flight Delays, describes how data scientists and developers can build such an application. The app uses the Apache Spark machine learning library (MLlib), fueled by publicly available airplane flight data and enriched with weather data, to predict flight delays caused by weather conditions. When a delay is likely, the app can also help determine the degree to which the flight will be delayed. 
To build the app, you’ll learn how to use a get-build-analyze methodology and the IBM Analytics for Apache Spark service, which includes an interactive Jupyter Notebook. Using this methodology you’ll learn the following from three key sections of the ebook: 
  • Get data from various sources, including weather and flight patterns. 
  • Build the data collected, and test a predictive model.
  • Analyze the data for business advantage, and quantify the impact of weather on flight delays. 
Download this ebook to get started building this flight prediction application, and gain inspiration for your next data science projects.  

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