Predictive Analytics: Applying Clustering to Soil Features & Conditions

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. 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.