Predictive Analytics: Performing Prediction Using Regression
Predictive Analytics 2022 | Intermediate
- 8 videos | 54m 25s
- Includes Assessment
- Earns a Badge
In agriculture, accurately assessing crop yield in advance can help farmers effectively plan ahead, allocate labor and capital, and plan for crop transportation logistics. Machine learning (ML) can be used to account for the many factors that drive yields. In this course, work with data consisting of blueberry plant information and climate factors to predict yield. Next, learn how to visualize univariate relationships and bivariate correlations and perform linear regression. Finally, practice performing feature selection for the regression model and view the score of importance and model on a subset for different data attributes. Upon completion, you'll be able to use regression techniques to predict agricultural yields, identify real-world and statistical relationships in the data, and differentiate between various regression models.
WHAT YOU WILL LEARN
discover the key concepts covered in this courseimport data on wild blueberry yieldsexamine data for blueberry yieldsperform transformations on blueberry yield data
predict blueberry yield using linear regressionimprove crop yield prediction using different machine learning (ML) modelsidentify attributes that help with yield predictionsummarize the key concepts covered in this course
IN THIS COURSE
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