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Make better predictions with spatial algorithms in Oracle Machine Learning for Python (OML4Py) on Autonomous Database

About This Workshop

Youtube Video

About This Workshop
A collection of spatial algorithms are now available with Oracle Machine Learning for Python (OML4Py), enabling you to incorporate location into your data science and machine learning-based solutions. Spatial algorithms can enhance ML with predictive models that account for geographical variations of predictive factors, detection of spatial patterns such as geographical hotspots and outliers, and more. In this workshop you work with vehicle registration and demographic data to create predictive models for electric vehicle adoption incorporating the effects of location. You will work with several spatial features of OML4Py: 1) spatial data prep and pre/post processing , 2) exploratory spatial analysis and SQL operations (i.e. Python API for Oracle Spatial), 3) spatial algorithms for predictions and pattern detection and 4) map rendering of spatial data in OML notebooks. Through this exercise, you will gain an understanding and appreciation for the role that spatial algorithms play in optimizing the performance of your ML models.

Workshop Info

1 hour, 15 minutes
  • Load data 
  • Explore and cleanse data
  • Engineer spatial features
  • Train and test spatial models
  • Evaluate and compare models
  • Familiarity with Python and ML concepts
  • Some understanding of cloud and database terms is helpful
  • Familiarity with Oracle Cloud Infrastructure (OCI) is helpful

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