Predictive Analytics in Agriculture Competency (Intermediate Level)

  • 21m
  • 21 questions
The Predictive Analytics in Agriculture Competency (Intermediate Level) benchmark measures your ability to identify how to perform classification using machine learning on agriculture data. You will be evaluated on your skills in applying clustering to soil features and conditions and performing prediction using regression. A learner who scores high on this benchmark demonstrates that they have experience performing predictive analytics in agriculture.

Topics covered

  • classify types using machine learning (ML)
  • cluster crop data using agglomerative clustering
  • examine data for blueberry yields
  • fine-tune the clustering of climatic data
  • identify attributes that help with yield prediction
  • import data on wild blueberry yields
  • improve crop yield prediction using different machine learning (ML) models
  • optimize the clustering of climatic data
  • perform feature selection for classification
  • perform k-means clustering on climatic data
  • perform transformations on blueberry yield data
  • predict blueberry yield using linear regression
  • select the most important attributes for classification
  • set up a classifier
  • set up a climatic condition clustering model
  • set up clustering for climatic data
  • set up data for performing classification
  • transform crop data for clustering
  • view attribute relationships in the data
  • view relationships between climatic conditions
  • visualize data about a crop's climatic conditions