Predictive Analytics: Applying Clustering to Soil Features & Conditions

Predictive Analytics 2022    |    Intermediate
  • 12 Videos | 1h 24m 48s
  • Includes Assessment
  • Earns a Badge
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

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

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