Predictive Analytics: Performing Prediction Using Regression
Predictive Analytics
| 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
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Discover the key concepts covered in this course
Import data on wild blueberry yields
Examine data for blueberry yields
Perform transformations on blueberry yield data -
Predict blueberry yield using linear regression
Improve crop yield prediction using different machine learning (ml) models
Identify attributes that help with yield prediction
Summarize the key concepts covered in this course
IN THIS COURSE
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1m 33s
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7m 2s
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3. Examining Attributes in Yield Data6m 37s
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4. Transforming Yield Data7m 26s
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5. Performing Yield Prediction Using Linear Regression11m 21s
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6. Improving Yield Prediction with Different ML Models10m 4s
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7. Identifying Attributes Relevant to Yield Prediction8m 43s
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8. Course Summary1m 39s
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