Predictive Analytics: Applying Clustering to Soil Features & Conditions

Predictive Analytics    |    Intermediate
  • 12 videos | 1h 24m 48s
  • Includes Assessment
  • Earns a Badge
Rating 3.0 of 3 users Rating 3.0 of 3 users (3)
The question of what crops ought to be planted per growing season for a given patch of land is extremely important. An important related question is which type of crop fits most easily with the soil and climatic conditions. Machine learning (ML) models like clustering can help answer this question using data from other farms. In this course, work with soil data consisting of field climate conditions. Next, learn how to use charts to view univariate information and the relationships between attributes. Finally, discover how to perform k-means and agglomerative clustering on data. Upon completion, you'll be able to apply clustering to data, identify links between clusters identified by ML algorithms and the crops cultivated in them, and differentiate k-means and agglomerative clustering.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Explore data in a data frame about crops and the climates in which they are grown
    Visualize data about a crop's climatic conditions
    View relationships between climatic conditions
    Transform crop data for clustering
    Set up clustering for climatic data
  • Optimize the clustering of climatic data
    Perform k-means clustering on climatic data
    Set up a climatic condition clustering model
    Fine-tune the clustering of climatic data
    Cluster crop data using agglomerative clustering
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 37s
  • 7m 18s
  • Locked
    3.  Visualizing Data about Conditions
    8m 28s
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    4.  Viewing Relationships between Conditions
    7m 32s
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    5.  Transforming Data for Clustering
    7m 45s
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    6.  Setting Up Clustering for Data
    6m 46s
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    7.  Optimizing Data Clustering
    9m 15s
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    8.  Performing K-means Clustering on Data
    10m 34s
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    9.  Setting Up a Condition Clustering Model
    7m 9s
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    10.  Fine-tuning the Clustering of Data
    6m 54s
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    11.  Clustering Data Using Agglomerative Clustering
    9m 5s
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    12.  Course Summary
    2m 27s

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