Predictive Analytics: Performing Prediction Using Regression

Predictive Analytics    |    Intermediate
  • 8 videos | 54m 25s
  • Includes Assessment
  • Earns a Badge
Rating 4.8 of 5 users Rating 4.8 of 5 users (5)
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 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

  • 1m 33s
  • 7m 2s
  • Locked
    3.  Examining Attributes in Yield Data
    6m 37s
  • Locked
    4.  Transforming Yield Data
    7m 26s
  • Locked
    5.  Performing Yield Prediction Using Linear Regression
    11m 21s
  • Locked
    6.  Improving Yield Prediction with Different ML Models
    10m 4s
  • Locked
    7.  Identifying Attributes Relevant to Yield Prediction
    8m 43s
  • Locked
    8.  Course Summary
    1m 39s

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